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13.1 Oral versus Written Language

Learning objectives.

  • Understand the importance of language.
  • Explain the difference between denotative and connotative definitions.
  • Understand how denotative and connotative definitions can lead to misunderstandings.
  • Differentiate between oral and written language.

Group meeting on some comfortable bean bags

Clemsonunivlibrary – group meeting – CC BY-NC 2.0.

When we use the word “language,” we are referring to the words you choose to use in your speech—so by definition, our focus is on spoken language. Spoken language has always existed prior to written language. Wrench, McCroskey, and Richmond suggested that if you think about the human history of language as a twelve-inch ruler, written language or recorded language has only existed for the “last quarter of an inch” (Wrench, et al., 2008). Furthermore, of the more than six thousand languages that are spoken around the world today, only a minority of them actually use a written alphabet (Lewis, 2009). To help us understand the importance of language, we will first look at the basic functions of language and then delve into the differences between oral and written language.

Basic Functions of Language

Language is any formal system of gestures, signs, sounds, and symbols used or conceived as a means of communicating thought. As mentioned above, there are over six thousand language schemes currently in use around the world. The language spoken by the greatest number of people on the planet is Mandarin; other widely spoken languages are English, Spanish, and Arabic (Lewis, 2009). Language is ultimately important because it is the primary means through which humans have the ability to communicate and interact with one another. Some linguists go so far as to suggest that the acquisition of language skills is the primary advancement that enabled our prehistoric ancestors to flourish and succeed over other hominid species (Mayell, 2003).

In today’s world, effective use of language helps us in our interpersonal relationships at home and at work. Using language effectively also will improve your ability to be an effective public speaker. Because language is an important aspect of public speaking that many students don’t spend enough time developing, we encourage you to take advantage of this chapter.

One of the first components necessary for understanding language is to understand how we assign meaning to words. Words consist of sounds (oral) and shapes (written) that have agreed-upon meanings based in concepts, ideas, and memories. When we write the word “blue,” we may be referring to a portion of the visual spectrum dominated by energy with a wavelength of roughly 440–490 nanometers. You could also say that the color in question is an equal mixture of both red and green light. While both of these are technically correct ways to interpret the word “blue,” we’re pretty sure that neither of these definitions is how you thought about the word. When hearing the word “blue,” you may have thought of your favorite color, the color of the sky on a spring day, or the color of a really ugly car you saw in the parking lot. When people think about language, there are two different types of meanings that people must be aware of: denotative and connotative.

Denotative Meaning

Denotative meaning is the specific meaning associated with a word. We sometimes refer to denotative meanings as dictionary definitions. The definitions provided above for the word “blue” are examples of definitions that might be found in a dictionary. The first dictionary was written by Robert Cawdry in 1604 and was called Table Alphabeticall . This dictionary of the English language consisted of three thousand commonly spoken English words. Today, the Oxford English Dictionary contains more than 200,000 words (Oxford University Press, 2011).

Conotative Meaning

Connotative meaning is the idea suggested by or associated with a word. In addition to the examples above, the word “blue” can evoke many other ideas:

  • State of depression (feeling blue)
  • Indication of winning (a blue ribbon)
  • Side during the Civil War (blues vs. grays)
  • Sudden event (out of the blue)

We also associate the color blue with the sky and the ocean. Maybe your school’s colors or those of your archrival include blue. There are also various forms of blue: aquamarine, baby blue, navy blue, royal blue, and so on.

Some miscommunication can occur over denotative meanings of words. For example, one of the authors of this book recently received a flyer for a tennis center open house. The expressed goal was to introduce children to the game of tennis. At the bottom of the flyer, people were encouraged to bring their own racquets if they had them but that “a limited number of racquets will be available.” It turned out that the denotative meaning of the final phrase was interpreted in multiple ways: some parents attending the event perceived it to mean that loaner racquets would be available for use during the open house event, but the people running the open house intended it to mean that parents could purchase racquets onsite. The confusion over denotative meaning probably hurt the tennis center, as some parents left the event feeling they had been misled by the flyer.

Although denotatively based misunderstanding such as this one do happen, the majority of communication problems involving language occur because of differing connotative meanings. You may be trying to persuade your audience to support public funding for a new professional football stadium in your city, but if mentioning the team’s or owner’s name creates negative connotations in the minds of audience members, you will not be very persuasive. The potential for misunderstanding based in connotative meaning is an additional reason why audience analysis, discussed earlier in this book, is critically important. By conducting effective audience analysis, you can know in advance how your audience might respond to the connotations of the words and ideas you present. Connotative meanings can not only differ between individuals interacting at the same time but also differ greatly across time periods and cultures. Ultimately, speakers should attempt to have a working knowledge of how their audiences could potentially interpret words and ideas to minimize the chance of miscommunication.

Twelve Ways Oral and Written Language Differ

A second important aspect to understand about language is that oral language (used in public speaking) and written language (used for texts) does not function the same way. Try a brief experiment. Take a textbook, maybe even this one, and read it out loud. When the text is read aloud, does it sound conversational? Probably not. Public speaking, on the other hand, should sound like a conversation. McCroskey, Wrench, and Richmond highlighted the following twelve differences that exist between oral and written language:

  • Oral language has a smaller variety of words.
  • Oral language has words with fewer syllables.
  • Oral language has shorter sentences.
  • Oral language has more self-reference words ( I , me , mine ).
  • Oral language has fewer quantifying terms or precise numerical words.
  • Oral language has more pseudoquantifying terms ( many , few , some ).
  • Oral language has more extreme and superlative words ( none , all , every , always , never ).
  • Oral language has more qualifying statements (clauses beginning with unless and except ).
  • Oral language has more repetition of words and syllables.
  • Oral language uses more contractions.
  • Oral language has more interjections (“Wow!,” “Really?,” “No!,” “You’re kidding!”).
  • Oral language has more colloquial and nonstandard words (McCroskey, et al., 2003).

These differences exist primarily because people listen to and read information differently. First, when you read information, if you don’t grasp content the first time, you have the ability to reread a section. When we are listening to information, we do not have the ability to “rewind” life and relisten to the information. Second, when you read information, if you do not understand a concept, you can look up the concept in a dictionary or online and gain the knowledge easily. However, we do not always have the ability to walk around with the Internet and look up concepts we don’t understand. Therefore, oral communication should be simple enough to be easily understood in the moment by a specific audience, without additional study or information.

Key Takeaways

  • Language is important in every aspect of our lives because it allows people to communicate in a manner that enables the sharing of common ideas.
  • Denotative definitions are the agreed-upon meanings of words that are often found in dictionaries, whereas connotative definitions involve individual perceptions of words.
  • Misunderstandings commonly occur when the source of a message intends one denotative or connotative meaning and the receiver of the message applies a different denotative or connotative meaning to the same word or words.
  • Oral language is designed to be listened to and to sound conversational, which means that word choice must be simpler, more informal, and more repetitive. Written language uses a larger vocabulary and is more formal.
  • Find a magazine article and examine its language choices. Which uses of language could be misunderstood as a result of a reader’s connotative application of meaning?
  • Think of a situation in your own life where denotative or connotative meanings led to a conflict. Why do you think you and the other person had different associations of meaning?
  • Read a short newspaper article. Take that written article and translate it into language that would be orally appropriate. What changes did you make to adjust the newspaper article from written to oral language? Orally present the revised article to a classmate or friend. Were you successful in adapting your language to oral style?

Lewis, M. P. (2009). Ethnologue (16th ed.). Retrieved from http://www.ethnologue.com/ethno_docs/distribution.asp?by=size .

Mayell, H. (2003, February). When did “modern” behavior emerge in humans? National Geographic News . Retrieved from http://news.nationalgeographic.com/news/2003/02/0220_030220_humanorigins2.html .

McCroskey, J. C., Wrench, J. S., & Richmond, V. P. (2003). Principles of public speaking . Indianapolis, IN: The College Network.

Oxford University Press. (2011). How many words are there in the English language? Retrieved from http://oxforddictionaries.com/page/howmanywords

Wrench, J. S., McCroskey, J. C., & Richmond, V. P. (2008). Human communication in everyday life: Explanations and applications . Boston, MA: Allyn & Bacon, p. 304.

Stand up, Speak out Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Written Language

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Written communication

Written language is the written form of communication which includes both reading and writing. Although written language may at first be considered to simply be oral language in its written form, the two are quite different in that oral language rules are innate whereas written language is acquired through explicit education.

Written language, whether reading or writing, requires basic language abilities. These include phonological processing (understanding that words are made of discrete sounds, then associating letters with these sounds, i.e., decoding), vocabulary, and syntax (grammar). Skilled reading and writing further require an awareness of what is being read or written in order to construct meaning. Given characteristic and varying difficulties in language in individuals with autism spectrum disorders (ASD), the bidirectional relationship between oral and written language poses challenges to written language development.


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References and Readings

Berninger, V. W., & Winn, W. D. (2006). Implications of advancements in brain research and technology for writing development, writing instruction, and educational evolution. In C. MacArthur, S. Graham, & J. Fitzgerald (Eds.), The writing handbook (pp. 96–114). New York: The Guilford Press.

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Brown, H. M., & Klein, P. D. (2011). Writing, Asperger Syndrome and theory of mind. Journal of Autism and Developmental Disorders, 1 (1/5), 1–11. doi:10.1007/s10803-010-1168-7.

Hooper, S. R. (2009). Biological processes underlying written language acquisition . Encyclopedia of Language and Literacy Development (pp. 1–9). London, ON: Canadian Language and Literacy Research Network. Retrieved July 26, 2011, from http://www.literacyencyclopedia.ca/pdfs/topic.php?topId=288

Kushki, A., Chau, T., & Anagnostou, E. (2011). Handwriting difficulties in children with autism spectrum disorders: A scoping review. Journal of Autism and Developmental Disorders, 1 (2/25), 1–11. doi:10.1007/s10803-011-1206-0.

Nation, K., Clarke, P., Wright, B., & Williams, C. (2006). Patterns of reading ability in children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 36 (7), 911–919. doi:10.1007/s10803-006-0130-1.

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Dr. Diana B. Newman ( Assistant Professor )

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Newman, D.B. (2013). Written Language. In: Volkmar, F.R. (eds) Encyclopedia of Autism Spectrum Disorders. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1698-3_1125

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Speaking, writing and reading are integral to everyday life, where language is the primary tool for expression and communication. Studying how people use language – what words and phrases they unconsciously choose and combine – can help us better understand ourselves and why we behave the way we do.

Linguistics scholars seek to determine what is unique and universal about the language we use, how it is acquired and the ways it changes over time. They consider language as a cultural, social and psychological phenomenon.

“Understanding why and how languages differ tells about the range of what is human,” said Dan Jurafsky , the Jackson Eli Reynolds Professor in Humanities and chair of the Department of Linguistics in the School of Humanities and Sciences at Stanford . “Discovering what’s universal about languages can help us understand the core of our humanity.”

The stories below represent some of the ways linguists have investigated many aspects of language, including its semantics and syntax, phonetics and phonology, and its social, psychological and computational aspects.

Understanding stereotypes

Stanford linguists and psychologists study how language is interpreted by people. Even the slightest differences in language use can correspond with biased beliefs of the speakers, according to research.

One study showed that a relatively harmless sentence, such as “girls are as good as boys at math,” can subtly perpetuate sexist stereotypes. Because of the statement’s grammatical structure, it implies that being good at math is more common or natural for boys than girls, the researchers said.

Language can play a big role in how we and others perceive the world, and linguists work to discover what words and phrases can influence us, unknowingly.

How well-meaning statements can spread stereotypes unintentionally

New Stanford research shows that sentences that frame one gender as the standard for the other can unintentionally perpetuate biases.

Algorithms reveal changes in stereotypes

New Stanford research shows that, over the past century, linguistic changes in gender and ethnic stereotypes correlated with major social movements and demographic changes in the U.S. Census data.

Exploring what an interruption is in conversation

Stanford doctoral candidate Katherine Hilton found that people perceive interruptions in conversation differently, and those perceptions differ depending on the listener’s own conversational style as well as gender.

Cops speak less respectfully to black community members

Professors Jennifer Eberhardt and Dan Jurafsky, along with other Stanford researchers, detected racial disparities in police officers’ speech after analyzing more than 100 hours of body camera footage from Oakland Police.

How other languages inform our own

People speak roughly 7,000 languages worldwide. Although there is a lot in common among languages, each one is unique, both in its structure and in the way it reflects the culture of the people who speak it.

Jurafsky said it’s important to study languages other than our own and how they develop over time because it can help scholars understand what lies at the foundation of humans’ unique way of communicating with one another.

“All this research can help us discover what it means to be human,” Jurafsky said.

Stanford PhD student documents indigenous language of Papua New Guinea

Fifth-year PhD student Kate Lindsey recently returned to the United States after a year of documenting an obscure language indigenous to the South Pacific nation.

Students explore Esperanto across Europe

In a research project spanning eight countries, two Stanford students search for Esperanto, a constructed language, against the backdrop of European populism.

Chris Manning: How computers are learning to understand language​

A computer scientist discusses the evolution of computational linguistics and where it’s headed next.

Stanford research explores novel perspectives on the evolution of Spanish

Using digital tools and literature to explore the evolution of the Spanish language, Stanford researcher Cuauhtémoc García-García reveals a new historical perspective on linguistic changes in Latin America and Spain.

Language as a lens into behavior

Linguists analyze how certain speech patterns correspond to particular behaviors, including how language can impact people’s buying decisions or influence their social media use.

For example, in one research paper, a group of Stanford researchers examined the differences in how Republicans and Democrats express themselves online to better understand how a polarization of beliefs can occur on social media.

“We live in a very polarized time,” Jurafsky said. “Understanding what different groups of people say and why is the first step in determining how we can help bring people together.”

Analyzing the tweets of Republicans and Democrats

New research by Dora Demszky and colleagues examined how Republicans and Democrats express themselves online in an attempt to understand how polarization of beliefs occurs on social media.

Examining bilingual behavior of children at Texas preschool

A Stanford senior studied a group of bilingual children at a Spanish immersion preschool in Texas to understand how they distinguished between their two languages.

Predicting sales of online products from advertising language

Stanford linguist Dan Jurafsky and colleagues have found that products in Japan sell better if their advertising includes polite language and words that invoke cultural traditions or authority.

Language can help the elderly cope with the challenges of aging, says Stanford professor

By examining conversations of elderly Japanese women, linguist Yoshiko Matsumoto uncovers language techniques that help people move past traumatic events and regain a sense of normalcy.

August 24, 2023

What’s the World’s Oldest Language?

Debate rages over which languages can claim to have the earliest origin

A closeup of ancient cuneiform script carved into clay or stone

The earliest documented writing comes from languages that used cuneiform script.

scaliger/Getty Images

The globe hums with thousands of languages. But when did humans first lay out a structured system to communicate, one that was distinct to a particular area?

Scientists are aware of more than 7,100 languages in use today. Nearly 40 percent of them are considered endangered, meaning they have a declining number of speakers and are at risk of dying out. Some languages are spoken by fewer than 1,000 people, while more than half of the world’s population uses one of just 23 tongues.

These languages and dead ones that are no longer spoken weave together millennia of human interactions. That means the task of determining the world’s oldest language is more than a linguistic curiosity. For instance, in order to decipher clay tablet inscriptions or trace the evolution of living tongues, linguists must grapple with questions that extend beyond language. In doing so, their research reveals some of the secrets of ancient civilizations and even sparks debates that blend science and culture.

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“Ancient languages, just like contemporary languages, are crucial for understanding the past. We can trace the history of human migrations and contacts through languages. And in some cases, the language information is our only reliable source of information about the past,” says Claire Bowern, a Professor of Linguistics at Yale University. “The words that we can trace back through time give us a picture of the culture of past societies.”

Language comes in different forms—including speech, gestures and writing—which don’t all leave conclusive evidence behind. And experts use different approaches to determine a language’s age.

Tracing the oldest language is “a deceptively complicated task,” says Danny Hieber, a linguist who studies endangered languages. One way to identify a language’s origins is to find the point at which a single tongue with different dialects became two entirely distinct languages, such that people speaking those dialects could no longer understand each other. “For example, how far back in history would you need to go for English speakers to understand German speakers?” he says. That point in time would mark the origins of English and German as distinct languages, branching off from a common proto-Germanic language.

Alternatively, if we assume that most languages can be traced back to an original, universal human language, all languages are equally old. “You know that your parents spoke a language, and their parents spoke a language, and so forth. So intuitively, you’d imagine that all languages were born from a single origin,” Hieber says.

But it’s impossible to prove the existence of a proto-human language—the hypothetical direct ancestor of every language in the world. Accordingly, some linguists argue that the designation of the “oldest language” should belong to one with a well-established written record.

Many of the earliest documented examples of writing come from languages that used cuneiform script, which featured wedge-shaped characters impressed into clay tablets. Among these languages are Sumerian and Akkadian, both dating back at least 4,600 years. Archaeologists have also found Egyptian hieroglyphs carved into the tomb of Pharaoh Seth-Peribsen that date to around the same historical period. The inscription translates to: “He has united the Two Lands for his son, Dual King Peribsen,” and it is considered the earliest-known complete sentence.

Historians and linguists generally agree that Sumerian, Akkadian and Egyptian are the oldest languages with a clear written record. All three are extinct, meaning they are no longer used and do not have any living descendants that can carry the language to the next generation.

As for the oldest language that is still spoken, several contenders emerge. Hebrew and Arabic stand out among such languages for having timelines that linguists can reasonably trace, according to Hieber. Although the earliest written evidence of these languages dates back only around 3,000 years, Hieber says that both belong to the Afroasiatic language family, whose roots trace back to 18,000 to 8,000 B.C.E., or about 20,000 to 10,000 years ago. Even with this broad time frame, contemporary linguists widely accept Afroasiatic as the oldest language family. But the exact point at which Hebrew and Arabic diverged from other Afroasiatic languages is heavily disputed.

Bowern adds Chinese to the list of candidates. The language likely emerged from Proto-Sino-Tibetan, which is also an ancestor to Burmese and the Tibetan languages, around 4,500 years ago, although the exact date is disputed. The earliest documented evidence of the Chinese writing system comes from inscriptions on tortoise shells and animal bones that date back to about 3,300 years ago. Modern Chinese characters weren’t introduced until centuries later, however.

Turn the clock back an additional one or two millennia, and the linguistic record becomes especially murky. Deven Patel, a professor of South Asia studies at the University of Pennsylvania, says the earliest written records of Sanskrit are ancient Hindu texts that were composed between 1500 and 1200 B.C.E. and are part of the Vedas, a collection of religious works from ancient India. “In my view, Sanskrit is the oldest continuous language tradition, meaning it’s still producing literature and people speak it, although it’s not a first language in the modern era,” Patel says.

Some linguists, however, argue that the appearance of Sanskrit was predated by Tamil, a Dravidian language that is still used by almost 85 million native speakers in southern India and Sri Lanka. Scientists have documented Tamil for at least 2,000 years. But scholars have contested the true age of the oldest surviving work of Tamil literature, known as the Tolkāppiyam, with estimates ranging from 7,000 to 2,800 years. “There are disputes among scholars about the precise date of ancient texts ascribed to Tamil and whether the language used is actually similar enough to modern Tamil to categorize them as the same language,” Patel says. “Tamil [speakers] have been especially [enthusiastic] in trying to separate the language as uniquely ancient.”

Disagreements about the age of Sanskrit and Tamil illustrate the broader issues in pinpointing the world’s oldest language. “To answer this question, we’ve seen people create new histories, which are as much political as they are scientific,” Patel says. “There are bragging rights associated with being the oldest and still evolving language.”

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Written language refers to a language that is written down and used for recording events, ideas and feelings. The opposite of written language is spoken language and there are a number of differences between the two. Accessing and exploiting the written word requires two key language skills: writing and reading. Without these two, especially reading, it becomes almost impossible to understand what has been written even though the majority of words will be understood aurally.

Writing developed spontaneously in a number of cultures around the world and spread or mixed with other writing systems. In the modern world, a number of scripts have come to dominate, the most common being the Latin script that dominates Western Europe, sub-Saharan Africa, the Americas and Oceania. There is also the Arabic script that dominates the Middle East and northern Africa and the Chinese script that dominates East Asia.

on written language

Many scripts owe their existence to pictographs. These are actual representations of objects, people and animals. It is supposed that cave paintings such as those at the Chauvet cave in France were used for education. These developed into Chinese hanzi and Egyptian hieroglyphs. It is thought that Hebrew, Latin, Greek and Arabic scripts are offshoots of Phoenician and Egyptian.

on written language

The development of a written language allowed communities and people to record stories such as Homer’s “Iliad” and the Sumerian “Epic of Gilgamesh.” It also allowed Kingdoms to interact as seen in the letters between the Egyptians and the Hittites and for Kingdoms to organize themselves bureaucratically. Many surviving scripts and inscriptions are used to record taxes, properties, wills and burials.

on written language

Written language was first and foremost used by the wealthy and the educated. In early medieval times, this was usually church priests and the odd king. This meant that written language became the language of the educated and did not necessarily represent the way normal people speak. This means, therefore, there is often a large distinction between written language and spoken language.

Modern technology has increased the gap between the two. As well as mixing in modern slang and terms into the written lexicon, modern technology has seen a fad where people abbreviate and contradict phrases. This has seen a new written vocabulary that includes ‘laugh out loud’ becoming ‘LOL.'

Archaic and dead languages only survive because of written records. Some of these are written in alphabets we do not understand; this includes Mayan. As a result, vocabularies are often incomplete and their pronunciations are guessed at best. Even in currently active languages such as English, it is impossible to know whether normal Anglo-Saxons spoke in the same way as their highly developed poets and writers.

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Omniglot - the online encyclopedia of writing systems & languages

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Writing systems by language

This is a list of the languages featured on Omniglot arranged by the writing systems with which they are written. This is not an exhaustive list of all the languages written with each writing system, but mainly the ones that appear on Omniglot.

The most widely used writing systems are the Latin, Cyrillic and Arabic alphabets. An index of all the languages of all the languages featured on this site is available in the language index .

Some languages have been written with a number of different writing systems over the years. For example, in Central Asia many languages were originally written with the Arabic alphabet, then switched to the Latin alphabet during the 1920s, then to the Cyrillic alphabet during the 1930s or 1940s. Some of them switched back to the Latin alphabet during the 1990s or in the early 21st century.

Writing systems used to write more than one language

Arabic , Armenian , Balinese , Baybayin , Burmese , Canadian Aboriginal Syllabics , Chinese , Coptic , Cuneiform , Cyrillic , Devanagari , Eastern Nagari / Bengali , Ge'ez (Ethiopic) , Greek , Georgian , Gujarati , Gurmukhi , Hebrew , Javanese , Japanese , Kannada , Kawi , Kharosthi , Kathi , Khojki , Lao , Latin , Lontara , Malayalam , Manchu , Mongolian , Mwangwego , N'ko , Odia (Oriya) , Ogham , Phags-pa , Pollard , Runic , Sharda , Soyombo , Syriac , Tai Viet , Takri , Tamil , Telugu , Thai , Tibetan , Tifinagh

Writing systems (mainly) used to write one language

Adlam , Ahom , Akkadian Cuneiform , Ancient Berber , Ancient Egyptian Demotic , Ancient Egyptian Hieratic , Ancient Egyptian Hieroglyphic , Ancient Latin , Aramaic , Avestan , Bamum , Bassa (Vah) , Batak , Beitha Kukju , Blackfoot , Borama , Brahmi , Buhid , Carian , Caroline Island Script , Carpathian Basin Rovas , Carrier , Caucasian Albanian , Celtiberian , Chakma , Cham , Cherokee , Chữ-nôm , Coorgi-Cox , Cree , Cypriot , Dalecarlian Runes , Dehong Dai , Deseret , Dhives Akuru , Elbasan , Elamite , Eskayan , Etruscan , Evela Akuru , Faliscan , Fraser , Glagolitic , Gondi , Gothic , Grantha , Hanunó'o , Iberian , Inutitut , Jurchen , Kayah Li , Khazarian Rovas , Khitan , Khmer , Kirat Rai , Korean (Hangeul) , Kpelle , Lanna , Lepcha , Limbu , Linear A , Linear B , Loma , Luwian , Lycian , Lydian , Mandaic , Manipuri , Marsiliana , Mayan , Mende , Meroïtic , Messapic , Middle Adriatic / South Picene , Middle Persian , Modi , Mon , Nabataean , Naskapi , Naxi , Ndjuka , New Tai Lue , North Picene , Nüshu , Oirat Clear Script , Ojibwe , Old Church Slavonic , Old Permic , Old Persian Cuneiform , Old Uyghur , Orkhon / Old Turkic , Oscan , Osmanya (Somali) , Pahawh Hmong , Pallava , Parthian , Phoenician , Phrygian , Psalter , Proto-Sinaitic / Proto-Canaanite , Punic , Ranjana , Redjang , Rongo Rongo , Sabaean , Samaritan , Santali , Sasak , Shan , Siddham , Sindhi , Székely-Hungarian Rovás (Hungarian Runes) , Sinhala , Sorang Sompeng , Saurashtra , Sumerian Cuneiform , Sundanese , Syloti-Nagri , Tagbanwa , Tangut , Thaana , Tocharian , Todhri , Tolong Siki , Tigalari , Ugaritic , Umbrian , Vai , Varang Kshiti , Yi , Zaghawa

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Writing systems : Abjads | Alphabets | Abugidas | Syllabaries | Semanto-phonetic scripts | Undeciphered scripts | Alternative scripts | Constructed scripts | Fictional scripts | Magical scripts | Index (A-Z) | Index (by direction) | Index (by language) | Index (by continent) | What is writing? | Types of writing system | Differences between writing and speech | Language and Writing Statistics | Languages

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Language: Its Origin and Ongoing Evolution

Ilia markov.

1 Department of Psychology, University of Houston, Houston, TX 77204, USA

2 Texas Institute for Measurement, Evaluation, and Statistics (TIMES), The University of Houston, Houston, TX 77204, USA

3 Center for Cognitive Sciences, Sirius University for Science and Technology, Sochi 354340, Russia

Kseniia Kharitonova

Elena l. grigorenko.

4 Baylor College of Medicine, Houston, TX 77030, USA

5 Child Study Center and Haskins Laboratories, Yale University, New Haven, CT 06520, USA

6 Rector’s Office, Moscow State University for Psychology and Education, Moscow 127051, Russia

Associated Data

Not applicable.

With the present paper, we sought to use research findings to illustrate the following thesis: the evolution of language follows the principles of human evolution. We argued that language does not exist for its own sake, it is one of a multitude of skills that developed to achieve a shared communicative goal, and all its features are reflective of this. Ongoing emerging language adaptations strive to better fit the present state of the human species. Theories of language have evolved from a single-modality to multimodal, from human-specific to usage-based and goal-driven. We proposed that language should be viewed as a multitude of communication techniques that have developed and are developing in response to selective pressure. The precise nature of language is shaped by the needs of the species (arguably, uniquely H. sapiens ) utilizing it, and the emergence of new situational adaptations, as well as new forms and types of human language, demonstrates that language includes an act driven by a communicative goal. This article serves as an overview of the current state of psycholinguistic research on the topic of language evolution.

1. Introduction

Research on language origin and evolution may be viewed as two of the most prominent research directions of the past few years. While these questions have been at the forefront of language science since its inception, only recently have we seen methodologies and techniques being developed that can provide answers backed with sufficient empirical evidence. The landscape of theoretical frameworks of language origin, the form in which it originated, and its worldwide dispersal has also been shifting in response to newly obtained evidence. The field of language evolution research can be described as currently coming of age while already equipped with a rich toolkit of methods for pursuits such as comparative research, investigating commonalities and differences between human language and animal communication systems, and studying cumulative cultural evolution of communication systems in experimental settings ( Dediu and de Boer 2016 ).

The aim of this article was to give a brief overview of the evolution of language, as well as to demonstrate how theoretical frameworks of language origin and evolution evolved with it. We sought to utilize the latest findings to argue that evolution is one of the central driving forces of the existence and development of human language. Language is a human skill, the nature and features of which are shaped in accordance with the needs of the species through continuous usage and adherence to communicative goals ( Grigorenko 2023 ). That nature is reflected through newly emergent language origin theories that move away from the innateness of language and provide plausible explanations of gradual language emergence from a multitude of other subsystems of communication. In regard to language modality, the main informational channel of origin, we intended to provide theoretical reasoning and evidence for the multimodal approach.

To illustrate that language is a skill that constantly undergoes changes due to various selective pressures, we aimed to explore three groups of factors that seem to transform language in the most significant ways: factors of the physical environment (such as aridity, vegetation, ambient temperatures, precipitation, latitude), socio-demographic factors (number of language users, geographic spread, degree of language contact, and the role of communicative situations), and technological advances (Internet, smartphones, and instant messaging). The latter group of factors is of most interest to us due to the changes that online communication is bringing about at a rate that has never been witnessed before. We propose that the evolution of language follows a similar pattern to the one outlined by recent research into general intelligence, promoting a more context-dependent and dynamic view of intelligence that is focused more radically than ever before on niche construction as a result of the cultural evolution ( Preiss 2022 ). The factors of the environment that influence language now reflect the changes brought along with the Anthropocene ( Anthropocene Working Group 2019 ), which further alters the ecological niche of our species, necessitating further adaptation.

While the argumentation provided in the present article is split into two main sections, covering the theories of origin and the evidence of evolution, all of it follows the main thesis of language emerging and evolving through usage; we deem this thesis to be the most prominent new direction in language research.

2. Origin of Language

There are more than 7000 living languages across the globe today ( Lewis 2009 ). To approach the topic of the ongoing evolution of language that spans millennia, we first need to determine its origin. However, the question by itself poses a challenge.

An important philosophical distinction collects two separate topics under the label “origin of language”: the origin of language faculty and the origin of languages ( Formigari 2013 ). The latter leads to a further question, namely, whether all world languages derive from one single “protolanguage”—i.e., monogenesis—or do several language families (e.g., Indo-European, Afro-Asiatic, Altaic) each derive from a different protolanguage—i.e., polygenesis ( Graffi 2019 ).

The theories relating to the emergence of language have been extensively debated for centuries, once leading to the infamous ban of the germane discussions by the Société de Linguistique de Paris in 1861. However, the field has seen a surge in this theorizing since the late 20th century and its considerable evolution over the past two decades ( Nölle et al. 2020b ). The focus of the debate has been on the innateness of language for many years. The central line of research that incorporates the traditional generative view ( Chomsky 1988 ), notions of innateness, universal grammar (UG), and the poverty of stimulus argument is largely classified as biolinguistics (for a more modern iteration of this framework, see ( Boeckx 2021 ; Bolhuis et al. 2014 ; Hauser et al. 2002 )). Such approaches argue that knowledge of language structures is impossible to extract from linguistic input; hence, it is suggested to be innate. An opposing point of view, referred to as “usage-based” or “emergentist” approaches, postulates that language emerged from its usage: “meaning is use—structure emerges from use” ( Tomasello 2009, p. 69 ); linguistic knowledge in these approaches proceeds via the abstraction and schematization of actual language use into fixed chunks, as well as more abstract linguistic patterns that become cognitively entrenched. These approaches, therefore, reject the notion of UG ( Pleyer and Hartmann 2019 ), instead utilizing the notion of the common communicative goal to explain language commonalities ( Arbib 2012 ). Notably, attempts to synthesize the opposing theories were few and far between. Such an attempt was undertaken by Pinker and Bloom ( 1990 ), where the authors put forth a compelling case against viewing language as a “spandrel”—an architectural allegory of a space formed at the intersection of other spaces, its shape therefore not being a significant trait on its own—arguing that certain constraints would not allow us to assume a non-adaptationist point of view, as “no adaptive organ can be adaptive in every aspect” (1990, p. 19).

In these controversies, one of the most contested topics is the question of causality, which is influenced by several factors: the problem of spurious correlation ( Roberts and Winters 2012 ), the universally concerning “replication crisis” ( Open Science Collaboration 2015 ), and a tendency to rely on indirect evidence ( Nölle et al. 2020b ). A novel solution for this contest is termed the “maximum robustness approach,” which, instead of focusing on simple causal relationships, aims to systematically construct more complex and coherent causal graphs ( Pearl 2000 ), incorporating all available evidence to form links between multiple variables. The CHIELD database, which is a repository of linguistic hypotheses produced in literature, was created to explore such graphs in order to find gaps or conflicting relationships, which can help to design empirical research addressing these issues and uncovering actual causal mechanisms ( Roberts et al. 2020 ). The database is public and functional: it is designed to be extendable by future researchers to ultimately become comprehensive and inclusive of as many languages as possible (that exist). Universal acceptance of a database such as this is the first step toward a realistic implementation of the maximum robustness approach.

To summarize, the current general trend for the linguistic field seems to move further and further away from notions of language innateness, although significant support for these viewpoints remains. In the scope of language evolution, usage-based frameworks allow for significantly more detailed and insightful investigations into the emergence of language. The same appears to be true for research into the modality of origin: this landscape of theories is also changing.

2.1. Modality of Origin

The debate on the modality of origin, which is the initial main channel carrying verbal information, also has a few contesting theories: according to the “gesture-first” view, “language evolved initially from manual gestures with vocal elements gradually added” ( Corballis 2011, p. 383 ). The “speech-first” view ( Dunbar 1997 ; MacNeilage 2008 ) argues for the pre-emergence of a vocal–auditory modality given its present-day dominance (for a full historical overview, see Fitch 2010 ). Modern theories argue for a multimodal emergence theory, incorporating complex interplay between auditory and visual channels ( Perlman 2017 ). Among newer ideas, “pantomime-first” was put forward as a distinct theoretical proposal ( Zlatev et al. 2017 ), which intrigues but does not provide much empirical evidence for its support. Another supporting usage-based account on multimodality comes from Levinson and Holler ( 2014 ), who propose that language normally occurs while embedded in a layered structure of multiple other channels of information. This view enables different phylogenetic and evolutionary origins to be assigned to each layer. Such holistic representation helps to bridge the gulf between the species, allowing us to recognize precursor adaptations such as turn-taking in current primates and the gestural skills of great apes as the first steps toward language formation, while the whole ensemble of language continues to be distinctively human.

One of the novel multimodal hypotheses is the mirror system hypothesis developed by Rizzolatti and Arbib ( 1998 ), which postulates that the mechanisms that support language in the human brain evolved atop a basic mechanism not originally related to communication: the mirror system, as the evolutionary basis for language, possesses a capacity to generate and recognize a set of actions. Arbib argues that the evolution of language is rooted in the execution and observation of hand movements, leading to the emergence of sign language, which was thereafter extended to speech. Complex imitation for hand movements evolved adaptively because of its utility in the social sharing of practical and manual skills. Skill sharing through imitation, such as grasping objects and using simple tools, existed long before language, being “more powerful than the call and gesture systems of nonhuman primates but lacking the full richness of modern human languages” ( Arbib 2012, p. 157 ).

Importantly, the origin theories based on the writing modality are characteristically absent, which is understandable given its (mostly) secondary nature to spoken language. This, however, is all too indicative of the attitudes to writing in language research prior to modern studies. The linguistic views on the emergence of writing were varied and controversial, echoing many general issues of the evolution of spoken language. The traditional outlook on writing systems since Aristotle was superficial, ostensibly viewing these systems as an optional, supplemental representation of spoken language. Moreover, writing was deemed a “wandering outcast of linguistics” ( Derrida 1976 ), leading to a suppression of research on writing. Similarly, for Saussure, written language was an object of suspicion, presenting a confounding and contaminating influence on language, going so far as to state that “to let go of the letter means a first step in the direction of truth” ( Saussure et al. 1986, p. 32 ). The views that were expressed during that time in the field were later ascribed to the “written language bias” ( Linell 2004 ).

Views that contested that bias started emerging in the mid-20th century from the historical ( Goody 1986 ) and anthropological ( McLuhan 1962 ) fields that, in turn, influenced studies of language to ascribe a more fundamental meaning to writing. One of the modern points of view from D.S. Olson proposed a special relationship of writing to the general machinery of language, which was influenced by those accounts and driven by developmental evidence ( Robinson et al. 1983 ). Olson’s most recent account postulated that reading and writing create a system of meta-representation concepts that contribute to consciousness, the formation of systematic thought, and rationality. In addition, some theories suggest that writing did not emerge as a secondary representation of spoken language but as the evolution of the token system for the purposes of goods exchanged or accounting ( Schmandt-Besserat 2012 ). The role of writing systems is similarly far from secondary according to the literacy hypothesis ( Goody and Watt 1963 ), and while it has received a lot of criticism on the matter of most aspects of civilized society preexisting and assimilating literacy at its advent, some scholars define influential “biases” ( Olson 2012 ) that may have contributed greatly to the cognitive and social development of the species. Such an impact of writing may be evidenced by tests of intelligence, including items that deal with vocabulary and the relationship between words, which test our capacity to participate in a literate environment ( Olson 2005 , as cited in Preiss and Sternberg 2006 ). Additionally, writing is essential to consider if adopting the adaptationist point of view, as the emergence of writing seems to possess several features characteristic of a Darwinian process ( Lock and Gers 2012 ).

Thus, while the dominating role of the vocal–auditory modality remains indisputable, progress in the field was made toward developing multimodal theories of language origin, which aid in unifying disparate evidence in support of different single-modality theories under a single governing principle.

2.2. Origin of Languages

The second question out of the pair laid out at the beginning of the section, namely, regarding “the origin of languages,” was mostly inquired upon in neighboring fields of inquiry and tied to the spread of human populations. A link between the human genome and the spread of languages has been debated ever since Darwin proposed that “a perfect pedigree of mankind… would afford the best classification of the various languages now spoken throughout the world” ( Darwin 1871 ). While some argued that the spread of languages is a good proxy for the dispersion of human populations ( Gray and Atkinson 2003 ; Mace and Holden 2005 ), opposition to this assumption was also persistent (e.g., Donohue and Denham 2010 ). Quantitative evidence supports both a general gene–language dispersion correspondence but also substantial (~20%) mismatches between 10 language families and corresponding populations ( Barbieri et al. 2022 ).

Globally, a consensus around the serial founder effect (SFE) process playing an important role in shaping global patterns of neutral genetic diversity is currently forming. This process entails a series of population splits, movements into an unoccupied territory, and subsequent isolation: beginning in Africa and proceeding through Eurasia into the Americas and Oceania. At the within-population level, it led to a steady decay in genetic diversity with increasing geographic distance from East Africa; at the between-population level, it led to a steady increase in genetic distance with increasing geographic distance ( Prugnolle et al. 2005 ; Ramachandran et al. 2005 ). The debate on the topic of language dispersion was later reignited by Atkinson ( 2011 ), who proposed that phoneme inventories in human languages had undergone a parallel SFE process based on the finding that the number of phonemes in 504 widespread languages decreased linearly with increasing geographic distance from Africa. Alternative assumptions for worldwide phonemic cline were tested using numerical simulations, showing that this pattern may be due to a repeated bottleneck effect and phonemic loss: low-density populations lost phonemes during the out-of-Africa dispersal of modern humans ( Pérez-Losada and Fort 2018 ). Creanza et al. ( 2015 ) further delved into this issue by performing joint and parallel analyses of phoneme counts in 2,082 languages and DNA microsatellite polymorphisms, which were used as signatures of human demographic history to calculate genetic distances between 246 populations. The results decisively vindicated Darwin’s proposal of human races and languages evolving in concert following a tree-like history of splits and isolation ( Darwin 1860 ) at the global level; however, it did not align with the SFE model with Africa as the center of origin, with it instead being more inclined toward a Eurasian-centered model. A more recent and novel analysis, which covered a cultural layer adjacent to language, namely, music, was carried out on a dataset of 152 societies (containing 1,054 songs from the public database The Global Jukebox in the form of raw coded Cantometrics data, 1,719 genomic profiles, and 152 languages); the analysis demonstrated weak links between music and language (R 2 <= 0.05), as well as with genetic distance and geographic proximity, in contrast to the much stronger relationships found between genes and geography: the results suggest that genes and culture are surprisingly decoupled ( Passmore et al. 2022 ). For the Indo-European family, Bouckaert et al. ( 2012 ) used Bayesian phylogeographic approaches with a dataset of basic vocabulary term lists from 103 ancient and contemporary Indo-European languages to model the expansion of the family, finding decisive support for an Anatolian origin over a steppe origin, with both the inferred timing and root location of the Indo-European language trees fitting with an agricultural expansion from Anatolia beginning 8000 to 9500 years ago. Certain linguistic methods, such as Bayesian phylogeographic approaches, that emerged from recent studies provided tentative answers to general questions of human prehistory: a recent study using lexical data and Bayesian phylogenetic methods placed the Austronesian origin in Taiwan approximately 5230 years ago and supported the hypothesis of “pulse-pause” expansion from Taiwan on the origin of the Austronesian settlers of the Pacific ( Gray et al. 2009 ). While being fairly recent for the field, the aforementioned techniques succeeded in helping linguists reclaim the issue of the origin of language as the viable research aim for future investigations aside from the origin of humanity research in neighboring fields.

To summarize, new technological and methodological advances have led to the most drastic changes in language evolution research. Large-scale investigations do require substantial resources, and interdisciplinary collaboration poses a challenge, but the results obtained contribute to significant advancements in the linguistic and neighboring fields in regard to the origin of language dispersal.

2.3. Neural Correlates of Language

The final important aspect of language origin studies, tangential to linguistics but central to psycholinguistics, is of utmost importance for the present essay: neural correlates of language. Evidence obtained from numerous previous studies that attempted to localize language within the brain is well established: the clinical studies of Broca ( Broca 1861 ) in the 19th century and Wernicke ( Wernicke [1874] 1994 ) in the 20th century, although contested now, served as an initial impulse for this. Similar to the debate on the language faculty origin in linguistic circles, we can note that the initial research findings that focused on narrow specificity of function were later extended to cover contesting evidence and new theoretical frameworks and have evolved into a multidimensional system. Through further publications on the topic emphasizing the importance of previously unaccounted-for brain regions (e.g., insula, Dronkers 1996 ), the model of choice for the end of the 20th century became the aphasia model (e.g., Obler and Gjerlow 1999 ). At the beginning of the 21st century, the model was further expanded into Broca-type and Wernicke-type aphasias in accordance with the impairment in one of two language comprehension axes ( Ardila 2011 , 2012 ), further solidifying the trend toward system complexity.

Modern models of language include numerous areas of the brain organized in multiple circuits within clusters of activation ( Ardila et al. 2016 ). One such model was constructed by Peter Hagoort ( 2005 ), who argued that the operation of distributed neural networks in Broca’s area and the left inferior frontal gyrus (LIFG) involves parallel processing of semantic, syntactic, and phonological information through three functional components: memory (long-term memory retrieval), unification (integrating information), and control (selecting a language “action”). Evidence from EEG and MEG studies helped to identify the specific temporal features of unification and memory retrieval components, arguing for neuronal synchronization that supports functional interrelatedness rather than strict domain specificity ( Bastiaansen and Hagoort 2006 ). These considerations are far from a theoretical conjecture nowadays, as they have been translated into presurgical planning ( Alemi et al. 2018 ). Additionally, there is evidence of neural multifunctionality for language networks, in particular, several frontal networks being linked to non-linguistic functions, such as mental rotation ( Jordan et al. 2001 ), musical syntax processing ( Maess et al. 2001 ), and arithmetic comprehension ( Baldo and Dronkers 2007 ). Such findings have driven researchers toward frameworks of multifunctional modularity and are instrumental for scholars developing usage-based approaches to language evolution.

Thus, despite initial findings that focused on the narrow specificity of function, the field has evolved to include numerous areas of the brain organized in multiple circuits within clusters of activation, in addition to the emerging evidence of neural multifunctionality for language networks.

3. Language Adaptation

Lupyan and Dale ( 2016 ) argued that observed linguistic differences arise not only from the accumulation of random changes due to the languages drifting apart but also may be reflective of the environment in which the language was developing. These environmental aspects that pressure languages into continuous diversification are social, physical, and technological in nature ( Lupyan and Dale 2016 ).

Just like birds develop different beaks adapting to different environments, languages and cultures might be undergoing similar changes ( Lupyan and Dale 2016 ). Charles Darwin, in “The Descent of Man, and Selection in Relation to Sex,” cited Max Müller to make a case for the evolution of language: “A struggle for life is constantly going on amongst the words and grammatical forms in each language. The better, the shorter, the easier forms are constantly gaining the upper hand, and they owe their success to their own inherent virtue.” ( Darwin 1871, p. 58 ). However, this idea of progress in linguistic evolution is considered dysfunctional by some ( Labov 1991 ; Mendívil-Giró 2018 ) due to its inability to explain the main patterns of linguistic structural diversity; a growing body of research asserts the contrary. The process of language diversification cannot be understood without considering the pressures that several factors (physical, ecological, and social) put on language users in different environments ( Bentz et al. 2018 ).

3.1. Ecological Adaptations

Similar to the communication systems of other species, language may be affected by ecological factors. Physiologically based predictions demonstrate that languages with complex tonality have generally not developed in very cold or otherwise desiccated climates, as air dryness decreases the control of the vocal folds and pitch production, and this, in turn, results in the absence of a (complex) tone system. The geographic–linguistic association operates within continents, major language families, and across language isolates ( Everett et al. 2015 ). However, replication of the study on a different dataset found it was not robust ( Roberts 2018 ). An analysis of over 4,000 language varieties showed a positive association between the language’s degree of reliance on vowels and the typical ambient humidity of a language’s native locale, which is consistent with other studies that focus on the link between aridity (i.e., the lack of effective moisture in a climate) and tonality of language ( Everett et al. 2015 ; Everett 2017 ), but the robustness was later found to be limited ( Roberts 2018 ).

Environments in which higher sound frequencies are less faithfully transmitted due to denser vegetation or higher ambient temperatures seem to be related to the greater use of sounds of lower frequencies (“more sonorous” languages). The results of Maddieson and Coupé ( 2015 ) point to a significant relationship between the “consonant-heaviness” of languages and several environmental factors, including tree cover and precipitation ( Maddieson and Coupé 2015 ). Further analysis of spoken samples did not find the relationship significant but identified that the percentage of sonorous material is correlated with the mean annual temperature in the area of the language ( Maddieson 2018 ). Studies that focused on the influence of temperature on languages find that languages spoken in cold, small regions tend to be more complex across a range of linguistic features, such as morphosyntactic complexity, linguistic diversity, word length, and consonant inventory ( Lewis and Frank 2016 ).

Another striking example is the observed partial correlation between latitude and the absence or presence of the word for the color blue ( Brown and Lindsey 2004 ; Lindsey and Brown 2002 ) due to the negative impact of ultraviolet light (UV-B) on the perception of the blue/green distinction (phototoxicity). In high-UV areas, languages without the word for blue prevail, which also correlates with the rates of blue-yellow color vision deficiency in these areas suggesting an evolutionary, physiological cause for both phenomena ( Brown and Lindsey 2004 ; Dediu et al. 2017 ).

A common criticism of the abovementioned studies is that they are correlational in nature, thus, do not contribute to the understanding of possible mechanisms that underlie linguistic evolutionary processes. In order to improve the methodological robustness of the studies, additional approaches, such as iterated learning, a historical case study, corpus studies, and studying individual speech, was suggested ( Roberts 2018 ). For this reason, several studies tried to experimentally investigate how environmental factors drive the emergence of linguistic conventions. Nölle et al. ( 2020a ) adapted the classical maze game task to confirm that subtle environmental motivations cause the emergence of different communicative conventions in an otherwise identical task, pointing to linguistic adaptations being highly sensitive to factors of the shared task environment. The authors speculated that these kinds of mechanisms identified at a local interactional level might contribute to the systematic global variation observed between different languages.

One of the most striking examples of linguistic adaptation to the environment is whistled languages. The main purpose of whistled languages is to facilitate spoken communication at great distances, but it is also used in other circumstances, such as secrecy, courtship, singing, and communication in noisy environments. Although they are always referred to as languages, they are considered a mode of speech because whistled languages are always based on a spoken language ( Meyer 2015 ).

Several hypotheses were put forward to explain the current existence of whistled languages. One of them posits that whistled languages are simply the vestigial remains of a widespread ancient phenomenon. This mode of speech could have been used by prehistoric hunter-gatherers for hunting in groups or signaling a danger in any type of environment ( Nettle and Romaine 2000 ). Another possible explanation is that the actual whistled languages are found only in a small minority of languages due to the erosion of traditional lifestyles and the relative ease of resorting to shouting because whistled speech would generally require more pressure to develop. This argument would be in favor of a key role played by significant environmental constraints in the emergence of whistled speech, which is supported by the observed systematic adaptation of whistled speech to typically constraining and geographically scattered ecological milieux ( Meyer 2015 ).

It is estimated that approximately 70–80 languages actively use their whistled mode of speech, but the number is rapidly declining due to modern technologies of communication, and most of them are endangered ( Meyer 2018 ). Evidently, whistled speech plays a strong functional role by complementing regular speech under unusual circumstances. Around the world, whistled forms of languages are associated with traditional activities, such as hunting, hill agriculture, or shepherding, in which individuals are relatively isolated and scattered across substantial areas of densely vegetated landscapes. In this type of environment, whistling has a clear advantage over speaking or shouting: acoustic signals can easily overcome ambient conditions and can travel longer distances. For example, La Gomera, one of Spain’s Canary Islands, holds the record for the longest distance of whistled conversations of approximately 1 km ( Meyer 2015 ); others have observed communications at approximately 8 km ( Busnel and Classe 1976 ).

The principle of whistled speech is straightforward: people articulate words while whistling, which involves acoustic reduction at the produced frequency level and selection of key salient phonetic cues for the corresponding spoken utterances. The resulting signal’s linguistic structure is identical to standard speech. Interestingly, even though the acoustic channel is reduced, whistled sentences remain highly intelligible to trained speakers ( Meyer 2015 ).

It was suggested that human whistled languages can serve as a model for understanding the coding of information in dolphin whistle communication. Comparing human and dolphin whistles could become a complementary test bench for the development of new methodologies for decoding whistled communication signals by providing new perspectives on structural and organizational aspects of encoding information ( Meyer et al. 2021 ).

Overall, exploring the connection between the structure of languages and the environment in which they are utilized is complicated by several issues. If the ecology of the area was able to influence the language at the stage of its emergence, the amount of information needed to make a conclusive statement about it is scarce. Additionally, most of the studies that focus on the link between environment and language are correlational in nature. Although the overall structural diversity of languages has not been linked with other types of diversity, some aspects, such as morphosyntactic complexity or consonant/vowel inventory, may be affected.

3.2. Socio-Demographic Adaptations

Apart from the effects of the physical environment and location, languages may be shaped by social and demographic factors. A statistical analysis of 2000 languages revealed strong relationships between the morphological complexity of a language and demographic/socio-historical factors, including the number of language users, geographic spread, and degree of language contact ( Lupyan and Dale 2010 ). It was suggested that languages spoken by large groups have a simpler inflectional morphology than languages spoken by smaller groups. Additionally, languages spoken by large groups are more likely to utilize lexical strategies in place of inflectional morphology when encoding evidentiality, negation, aspect, and possession ( Lupyan and Dale 2010 ). Based on these findings, Dale and Lupyan proposed the linguistic niche hypothesis , which describes the esoteric and exoteric niches for languages. The exoteric linguistic niche includes languages with large numbers of speakers (e.g., English, Swahili, and Hindi), which forces these languages to serve as a means of communication between strangers. Speakers of languages in the exoteric niche, compared with the esoteric niche, are more likely to be non-native speakers or have learned the language from non-native speakers and use the language to speak to individuals from different ethnic and/or linguistic backgrounds. The esoteric niche includes languages like Tatar, Elfdalian, and Algonquin ( Dale and Lupyan 2012 ; Lupyan and Dale 2010 , 2016 ). Linguistically, esoteric languages are more likely to be classified as isolating rather than fusional, have fewer grammatical categories marked on the verb, are more likely to encode negation via analytical strategies than using inflections, are less likely to have indefinite and definite articles, and are less likely to communicate distance distinction demonstratives ( Lupyan and Dale 2010 ). Further studies found only limited support for this hypothesis ( Lewis and Frank 2016 ) or did not find a strong relationship between the grammatical or statistical structure of language and the proportion of non-native speakers ( Koplenig 2019 ).

Winters et al. ( 2015 ) experimentally investigated the role of the communicative situation in which an utterance is produced and how it influences the emergence of three types of linguistic systems: underspecified languages, holistic systems, and systematic languages. Using a discrimination task in a communication game and manipulating whether the feature dimension shape was relevant or not in discriminating between two referents, it was established that different linguistic systems emerged. Furthermore, experimental languages gradually developed to encode information relevant to the communicative task in a given situational context. These results suggest that language systems adapt to their contextual niche over iterated learning.

Another interesting observation was made about the influence of population size on rates of language evolution. The rates of gain and loss of cognate words for basic vocabulary were analyzed in Polynesian languages. Larger populations were observed to have higher rates of gain of new words, while smaller populations had higher rates of word loss, which suggests that demographic factors may affect rates of language evolution and that rates of gain and loss are affected in different ways. However, the authors found that the results were strikingly consistent with general predictions of evolutionary models, paralleling positive selection in the case of greater rates of word gain in larger populations, and loss of diversity in small populations and greater rates of word loss ( Bromham et al. 2015 ).

An inquiry into language evolution that was made using estimates of cognate replacement for 200 concepts on an Indo-European language tree spanning 6–10 millennia to measure lexical evolution rates demonstrated that negative valence correlates with faster cognate replacement, even while controlling for frequency of use. Follow-up analyses showed that it is most robust for adjectives, does not consistently reach statistical significance for verbs, and never reaches significance for nouns ( Jackson et al. 2023 ).

Socio-demographic pressures are also known to lead to the emergence of new languages, the investigation of which can shed light on the process of language evolution. Among the newest languages of the world, Afrikaans is the youngest nationally recognized language, whose origin is tied to the establishment of Cape Colony in the Cape of Good Hope on the land of indigenous Khoekhoen people in the mid-16th century. The starting point of the Cape Colony is the landing of Jan van Riebeeck with three vessels to the Cape on 6th April 1652, which can also be thought of as the starting point of the Afrikaans language ( De Villiers 2012 ). The language developed through to the 18th century, becoming the language with the widest geographical, demographic, and racial distribution of all official languages of South Africa ( Webb 2003 ), and the debate on its origin was live and ongoing until the 20th century, mainly due the clash of political and ideological views that it instigated. Certain scholars at the time denied any indigenous influences on the language, while others insisted that the language was as much creolized as it was a product of West-Germanic sources. It has its roots in 17th-century Dutch but has been influenced by English, French, and German ( Hamans 2021 ), with traces of, amongst others, Malay and Portuguese ( Conradie and Groenewald 2014 ), and influenced by the pidgin talk of the indigenous Khoi and the San ( Hamans 2021 ).

While not as widely recognized as Afrikaans, Light Warlpiri would constitute the newest example of an emerging language. This language was discovered and documented by Carmel O’Shannessy ( 2005 ) and is thought to have originated sometime at the end of the previous century. It was spoken in the Warlpiri community of Lajamanu, in the Northern Territory of Australia, by children and young adults who are now mostly approximately 40 years of age ( O’Shannessy and Brown 2021 ). The language systematically combines elements of Warlpiri (a Pama-Nyungan language), Kriol (an English-based creole), and English, and was derived from the code-switched speech of parents to children following a particular pattern, where a Kriol pronoun and verb were inserted into a Warlpiri string, as part of a baby talk register ( O’Shannessy 2012 ). A new language emerged when young children internalized this pattern of speech as a single system, distinct from Warlpiri ( O’Shannessy 2020 ). In its formation, a speech pattern was further innovated in the verbal auxiliary system, namely, the =m “NONFUTURE” suffix ( O’Shannessy 2013 ). Light Warlpiri has enough systematic evidence to distinguish it as a separate language, for which its precursors act as lexifiers ( O’Shannessy 2005 ), and is nowadays the language of everyday interaction of the adult generation in the Lajamanu community.

To conclude, most typological studies suggest that linguistic diversity may be affected by several demographical and sociolinguistic factors. Social contexts can influence the way language is acquired and used, leading to linguistic structures that are specific to certain groups. Over time, this can result in variations in language usage that are reflected in typological patterns. This shows that language is not just a set of fixed rules and structures but rather a dynamic and adaptive skill that is shaped by the social context in which it is used. This ability to adapt to a social environment is a key feature of a language, and it helps to explain how language has evolved to meet the changing needs and contexts of different communities.

3.3. Technological Adaptations

Technology has drastically affected language and given rise to what is now commonly referred to as “text-speak” ( Al-Sharqi and Abbasi 2020 ). One of the biggest changes technology brought about is the speed of communication. The human brain is forced to process an unending stream of linguistic input and respond to it immediately. Christiansen and Charter called this the “Now-or-Never bottleneck,” which describes the immediacy with which the brain must compress and recode linguistic input ( Christiansen and Chater 2016 ). This bottleneck acts as a strong selection pressure against words and grammatical construction parsing, which, in real-time, is nearly impossible, especially when pressure is being put on the written language to communicate subtle nuances of face-to-face communication. The multi-faceted pressure inevitably influences the language that now has to undergo significant adaptive processes in order to fit the requirements of modern times, turning into what some deem “a natural experiment in the development of written communication” ( Varnhagen et al. 2010 ). Some of the ways that this adaptation manifests in the language are novel conventions of online communication, including acronyms, the modified use of typographic marks, and the use of emojis ( Lupyan and Dale 2016 ).

Emojis and emoticons are a group of symbolic combinations or pictures that are characteristic of online communication. An emoji (“☹”) is a graphic symbol that represents a wide variety of different things, ranging from complex facial expressions to concepts and ideas. It is thought to have developed from emoticons, i.e., representations of facial expressions usually comprised of various combinations of keyboard characters (“:)”). These symbols usually augment a message with non-verbal elements ( Novak et al. 2015 ).

Due to their growing popularity, emojis are used not only in online communications but are becoming integrated into an increasingly wider variety of contexts. Specifically, research is conducted to understand how emoji-enriched interfaces affect performance in the classroom ( Aliannejadi et al. 2021 ), marketing and advertisement ( Lee et al. 2021 ), and even their implications in law ( Goldman 2018 ).

Emojis (or “smileys”) are a unique phenomenon in terms of their nature and diverse functions in communication. On the one hand, emojis produce effects that are functionally similar to the response observed for facial expressions of emotion in face-to-face communication. They seem to affect the perceived emotional intensity of a message and accentuate its perceived valence by acting as nonverbal cues in digital communication ( Erle et al. 2021 ). On the other hand, emojis are closely connected with words. It was shown that the time course of semantic congruency effects on eye movements for emojis is similar to effects that were previously shown for words ( Barach et al. 2021 ). In the online public context, emojis alter the lexical diversity of text, which may point to a compensatory relationship between emojis and words in communication ( Feldman et al. 2021 ). Additionally, there is a link between emojis and gestures, with emojis denoting objects and activities interacting with logical operators in a text in a similar way as gestures do with speech ( Pierini 2021 ).

Lupyan and Dale argued that the divergence between conventional written languages (as well as online written communication) differs in many ways from the divergence between conventional spoken languages, for example, Dutch and Afrikaans. Both of these phenomena represent how languages (or language registers) adapt to the environments in which they are being used ( Lupyan and Dale 2016 ). Similar to Afrikaans and Dutch, it is feasible to assume that the written form of language diverges from the spoken form as an adaptation to this new environment, and the online form combines the features of the two forms into something that linguist John McWhorter described as a pure “linguistic miracle happening right under our noses” ( McWhorter 2013 ).

Shortcuts are one of the most prevalent features of the new “netspeak” ( Varnhagen et al. 2010 ). Common shortcuts in the context of instant messaging and online communication include abbreviations (prof—professor), initialisms and acronyms (LOL—laughing out loud, ASAP—as soon as possible), and logograms or “alphanumeronyms” (CUL8R—see you later).

One of the explanations of this process posits that users intuitively ignore uneconomical language rules and strive for cost-effectiveness, increasing the efficiency of the language orthography ( Lančarič 2016 ) and enriching it with new words and phrases that express complex feelings, emotions, or reactions (for example “wowzy” usually stands for extreme amazement or awe of a situation, thing, person, or place). Interestingly, some of the recently introduced units started to undergo a process of pragmaticalization, a subclass of grammaticalization, which possesses many similar features of grammaticalization processes but is distinguished from other subtypes by specific functions, domains, and syntactic integration ( Diewald 2011 ).

Pragmaticalized units may partially or completely lose their semantic meaning and move into a new pragmatic domain of function and meaning. In this sense, online communication is not only enriched by the spoken form of language with its abundance of discourse (or pragmatic) markers but gives rise to new netspeak-specific pragmatic features that slowly pave their way back into spoken language, making this interaction bidirectional and mutually enriching. This is only one of many aspects that is indicative of the emergence of a unique new hybrid register that fuses the full range of variants from the language use, namely, written, spoken, formal, informal, and vernacular variants ( Tagliamonte and Denis 2008 ).

One particular field able to inform the shortcut trend is quantitative linguistics, one of the aims of which is the development of statistical laws about language usage. Such laws can tell us a lot about speech and language efficiency principles, the most established among them being Zipf’s law of word frequency, which quantifies the frequency of occurrence of words, demonstrating that there is no unarbitrary way to distinguish between rare and common words ( Zipf 1949 ). This law is also rather common in complex systems where discrete units self-organize into groups or types ( Corral et al. 2019 ). Zipf’s law of brevity is sufficiently easier to observe through personal experience, stating that more frequent words tend to be shorter, and rarer words tend to be longer ( Bentz and Ferrer-i-Cancho 2016 ). A functional explanation for this law suggested by Zipf is the law of least effort, stating that it is human nature to want the greatest outcome at the least amount of work. Closely related to it is the Menzerath–Altmann law, which postulates that the size of the constituents (e.g., phonemes) of a construction (e.g., morpheme) decreases with the increasing size of the construction ( Altmann 1980 ). These two laws suggest that in human vocal communication, the maximization of coding efficiency and minimization of code length act as selective pressures to compress the elements supporting information ( Favaro et al. 2020 ). In addition to spoken language, there is evidence that laws of brevity hold in most writing systems (in a sample of 1262 texts and 986 different languages, see Bentz and Ferrer-i-Cancho 2016 ). However, there are exceptions, as with figurative signals, the frequency of which is shown to be positively correlated with complexity ( Miton and Morin 2019 ). The suggestion that these laws might extend beyond human language is substantivized by recent studies into vocal sequences of non-human primates ( Semple et al. 2010 ) and penguins ( Favaro et al. 2020 ), while some studies challenge that notion ( Bezerra et al. 2011 ).

Hence, technology has had a significant impact on language in recent years, giving rise to new forms of communication and new ways of using language. One of the most notable changes brought about by technology is the emergence of “text-speak,” which is a form of language that is characterized by the use of shorthand, abbreviations, and symbols in text-based communication. In this way, digital communication can be seen as a distinct form of language with unique features and conventions that are specific to the digital environment. It is not a replacement for spoken or written language, but rather, it is an additional way of communicating and conveying meaning, which is a skill that emerged in humans as a response to a new and all-encompassing technological environment with its constraints and possibilities.

4. Conclusions

The questions of the origin and evolution of language, apart from gaining new evidence for their resolution through the use of novel linguistic and psycholinguistic methods and interdisciplinary inquiries, also changed in their nature. Modern language research strays away from portraying human language as a unique anthropoid phenomenon, the expression of which has not been modified by selection pressure, instead viewing it through the lens of ongoing human evolution and strict adherence to communicative goals. In this light, language can be viewed as a toolbox, the contents of which change in accordance with the needs of the human species, which is the statement that we attempted to demonstrate through the review of the literature on language evolution and adaptation.

Manifold examples of languages adapting to and reflecting different aspects of the environment illustrate that linguistic diversification is not simply an accumulation of random changes over time. The most recent example of strong selective pressure affecting languages all over the world is the integration of instant communication technologies. This new phenomenon allows us to witness language modification in real time and better understand the underlying processes. It can already be confidently stated that human language is undergoing one of its most massive changes at this very moment while following the essential principles that governed its existence and development before the onset of the digital age.

Multimodal and usage-based emergence theories, in addition to more robust correlation and causation links, provide a framework that is apt to incorporate the majority of scientific knowledge about language. Modern neural correlate models of language processing further serve to illustrate the interconnectedness of language to other domains of cognition. Further steps in that direction of inquiry may only serve to elucidate how thoroughly integrated language is with all other types of human behavior.

We propose that the study of languages should not be confined to properties of particular languages and language in general but should incorporate a wider array of contributing factors that inevitably shape the way different species, including humans, communicate. This all-encompassing approach will provide more insights into the nature, structure, and functions of language in diverse environments and demographic contexts, as well as help to explain the way human communication adapts to and transforms in response to the pressures put forward by technological breakthroughs and societal transformations, along with the alterations in our species’ ecological niche in the Anthropocene era.

Funding Statement

The preparation of this essay was supported, in part, by a grant (R01HD109307) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), USA, to the University of Houston (principal investigator: Elena L. Grigorenko) and by funds from Sirius University, Russia. The content is solely our responsibility and does not necessarily reflect the views of the funders.

Author Contributions

Conceptualization, I.M., K.K. and E.L.G.; investigation, I.M. and K.K.; resources, E.L.G.; writing—original draft preparation, I.M. and K.K.; writing—review and editing, I.M., K.K. and E.L.G.; supervision, E.L.G.; funding acquisition, E.L.G. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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How to Identify and Translate the Language of any Text?

Can you identify the language of any text? There are 196 countries with around 7,164 languages. Surprisingly, we can recognize only 9-10 written languages and understand a maximum of 3-4 languages.

In this article, we will learn different ways by which you can practice identifying different languages of any text and ultimately convert it to understand its intent in your desired language.

How to Identify and Translate the Language of Any Text?

Learning a language is a great skill, especially when you like to travel across the world. Languages like ‘한국어’, ‘日本語’, and ‘中文’ are Korean, Japanese, and Chinese respective, but they look alike. We can’t learn so many languages, but we can surely practice identifying a language just by looking at it.

Method 1: Identify Language Using Browser’s Translate.

Search Engines like Google and Bing have their translation websites. just type – ‘ Google Translate, ‘Translate’, and ‘Bing Translate’ into your preferred browser. You will see various options to translate text, web pages, and documents into multiple languages.

Google Translate

Bing translate, method 2: language identification/translation tools.

Another way is to use language identification tools like What Language Is This?, Lexicool Tools, Yandex Translate, and DeepL Translate. All these tools support languages worldwide and can identify the language of any text you provide. Just paste your text into the text box and click the “Go” or “Scan” button to get the name of the language.

1. What language is this?


2. Laxicool Tool:


3. Yandex Translate:

This tool not only provides the name of the language but also allows you to translate the text into different languages.

  • Text translation:


  • Image translation: The best part about this tool is that it can even scan text from an image and convert the text, providing you with a new image in the required language.

4. DeepL Translator:

Method 3: web browsers built-in translation.

Web browsers like Google Chorme and Microsoft Edge provide built-in translation when you encounter a language that is not your default one. Here’s how easy it is to recognize the language and translate it into your desired one.

1. Google Chrome Built-in Translation

2. microsoft edge built-in translation, method 4: image recognition technology.

When you visit historical sites or museums in a foreign country, Google Lens can instantly translate informational signs or plaques into your preferred language. Similarly, Microsoft Lens can assist you to extract the text from an image.

Let’s see an example of how it works. Suppose I want to convert the text of the image, so can use Google Lens. Here are the steps.


Step 1: Open Google, and click on the Camera icon.


Step 2: Now, upload a image or take a photo of an image you can to translate..


Step 3: Select and copy the text from the image and click translate button.


Step 4: In the next window, you will see the detected language in the left panel and can select a language to translate your text from the right panel.


Method 5: Ask to AI Chatbots

AI chatbots like Gemini and Capilot are advanced AI assistants designed to help humans with various tasks through natural language interactions.

You can use these tools to help you identify languages. Simply input text in any language and prompt the tool to identify and translate it according to your command.


Ask Capilot


Method 6: Identify the Language Game

The fun way to learn is through games. There are online language identification games you can try to enhance your language detection and comprehension skills. One of such game is language squad.

Language Squad game tests your ability to identify languages by playing audio clips, and you need to select the correct language from the given options based on what you hear.


Not only this game, but you can also find more similar games or quizzes online to improve your language skills. Just search for ‘language identification games’ or ‘language quizzes’ and you’ll find plenty of options to practice and enhance your language abilities.

In conclusion, identifying languages has become easier with various online tools and techniques. Whether you use browser translation, language identification tools, chatbots, or language games, there are multiple ways to recognize and understand texts in different languages. Practicing these methods can enhance your language skills.

How to Identify and Translate the Language of any Text – FAQs

What are some popular language identification tools.

Some popular language identification tools include What Language Is This?, Lexicool Tools, Yandex Translate, and DeepL Translate. These tools can identify the language of any text you provide and often offer translation capabilities.

Can these language identification tools work with handwritten text?

Most of these tools are designed for printed or digital text. Handwriting recognition may not be accurate, especially for non-Latin scripts.

Are language identification games effective for learning new languages?

Yes, games make language learning fun, But they are not a substitute for structured language learning courses. However, they can help improve language recognition skills.

Can AI chatbots like Gemini and Capilot translate audio or speech as well?

A4. While these chatbots can identify and translate written text, audio/speech translation may not be supported or could have limited accuracy currently

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Deal written on a napkin that sent Lionel Messi to Barcelona sells at auction for $965,000

Lionel Messi of Barcelona

LONDON — The famous napkin that linked a young Lionel Messi to Barcelona sold for 762,400 pounds ($965,000) on Friday, British auction house Bonhams said.

An agreement in principle to sign the-then 13-year-old Messi was written on the napkin almost 25 years ago at a Barcelona tennis club. A more formal and detailed contract with the club followed soon after.

An undisclosed percentage of  the sale price  pays administrative fees for the online auction, in what’s called the buyer’s premium.

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Bonhams said the auction was on behalf of Horacio Gaggioli, an agent from Messi’s home country of Argentina who was part of the deal.

The contract language, written in blue ink, was intended to reassure the teenager’s father, Jorge Messi, that the deal would go through.

Jorge Messi had threatened to take his son back to Argentina because negotiations with Barcelona had stalled.

Image: The napkin on which the 13-year-old Argentinian football player Lionel Messi was promised his first contract with FC Barcelona

The napkin, containing the date Dec. 14, 2000, bears the signatures of Gaggioli, another agent, Josep Maria Minguella and Barcelona’s then-sporting director, Carles Rexach, who met at a tennis club.

Rexach had asked a waiter for paper and was given a blank napkin.

The starting price was 300,000 pounds ($379,000).

Messi spent nearly two decades with Barcelona after arriving from Argentina at 13 to play in its youth squads. He made his first-team debut in 2004 and played 17 seasons with the main squad. He helped the club win every major trophy including the Champions League four times and the Spanish league 10 times.

Messi left Barcelona for Paris Saint-Germain in the summer of 2021. He has since joined Inter Miami.

AI language translation startup DeepL nabs $300M on a $2B valuation to focus on B2B growth

on written language

More funding is being poured into startups focused on AI. DeepL , which builds automated text translation and writing tools that compete against the likes of Google Translate and Grammarly, said on Wednesday that it has raised an additional $300 million. It is now valued at $2 billion, post-money. 

This round, led by Index Ventures, underscores the frenetic interest that investors have in AI startups at the moment and how companies are capitalizing on that opportunity while they can. DeepL, which is still not profitable, was valued at $1 billion in January 2023 , when it raised just over $100 million. 

The new money will be used to drive more sales and marketing, as well as further research and development.

The company, based in Cologne, Germany, said it has more than 100,000 businesses and organizations using its tools. Given that this is just a tiny percentage of the company’s addressable market, the aim is to try to scale that significantly. 

CEO and founder Jarek Kutylowski told TechCrunch in an interview earlier this month that the company has largely grown organically so far, and it was looking to ramp up sales and marketing efforts to add more customers and expand what it does with those it already has. 

That highlights a key issue for AI companies targeting other businesses: Although many executives are pressing their teams to come up with strategies for how AI can be used in their organizations, many projects have failed to progress beyond the pilot or small deployment phase. Ramping that up will be of prime importance to AI tech vendors.

“Inbound is great, but we want to develop a stronger relationship with our customers,” he said. “We’re working hard on developing a better outbound function, because inbound is only going to get you so far. At some point, you have to start solving the problems together with your customers. The company’s transforming quite a bit into this enterprise direction, which is complicated and interesting for a research based company.”

Kutylowski said that about 60% of the company’s staff are technologists at the moment, and it will be hiring more non-technical personnel going forward. Indeed, balancing that with a focus on research will be one of DeepL’s big challenges. 

The startup supports 32 different languages at the moment, and it has been expanding its product portfolio steadily. The latest addition to the list is one very much focused on enterprises: DeepL Write Pro is described as “a writing assistant specifically tailored for business.” Customers signing up for DeepL’s tools include Zendesk, Nikkei, Coursera and Deutsche Bahn, it said.

“Companies want to have control over how their employees speak, right?” Kutylowski said.

However, DeepL faces potentially strong competition from a wide swath of companies: Some specialize in the same area, and platform companies like Google, Amazon and Microsoft already have operations in areas like translation and are looking to enhance them further with AI. 

Some of the newer, foundational AI companies like OpenAI or Anthropic have not made headway into the same space as DeepL yet, but there is an obvious opportunity for them, too. Some of these companies might not be focusing on translation and writing improvements right now, but making AI feel more seamless and “human” will continue to be a priority, so DeepL cannot rest on what it claims to be its leading position today.

ICONIQ Growth, Teachers’ Venture Growth, and previous backers IVP, Atomico and WiL also participated in the round.

“DeepL’s runaway success is a bit of an ‘open secret’ in the business community,” said Danny Rimer of Index Ventures in a statement. “The company is exceptionally thoughtful about creating cutting-edge AI products that deliver real and immediate value to their customers.”

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The biggest French startups in 2024 according to the French government

The French Secretary of State for the Digital Economy as of this year, Marina Ferrari, revealed this year’s laureates during VivaTech week in Paris. According to its promoters, this fifth…

The biggest French startups in 2024 according to the French government

Spotify to shut off Car Thing for good, leading users to demand refunds

Spotify is notifying customers who purchased its Car Thing product that the devices will stop working after December 9, 2024. The company discontinued the device back in July 2022, but…

Spotify to shut off Car Thing for good, leading users to demand refunds

X should bring back stars, not hide ‘likes’

Elon Musk’s X is preparing to make “likes” private on the social network, in a change that could potentially confuse users over the difference between something they’ve favorited and something…

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$6M fine for robocaller who used AI to clone Biden’s voice

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$6M fine for robocaller who used AI to clone Biden’s voice

Tesla lobbies for Elon and Kia taps into the GenAI hype

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App developer Crowdaa raises €1.2M and plans a US expansion

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Canva launches a proper enterprise product — and they mean it this time

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Chapter 56. Writing a Procedural Language Handler

All calls to functions that are written in a language other than the current “ version 1 ” interface for compiled languages (this includes functions in user-defined procedural languages and functions written in SQL) go through a call handler function for the specific language. It is the responsibility of the call handler to execute the function in a meaningful way, such as by interpreting the supplied source text. This chapter outlines how a new procedural language's call handler can be written.

The call handler for a procedural language is a “ normal ” function that must be written in a compiled language such as C, using the version-1 interface, and registered with PostgreSQL as taking no arguments and returning the type language_handler . This special pseudo-type identifies the function as a call handler and prevents it from being called directly in SQL commands. For more details on C language calling conventions and dynamic loading, see Section 36.10 .

The call handler is called in the same way as any other function: It receives a pointer to a FunctionCallInfoBaseData struct containing argument values and information about the called function, and it is expected to return a Datum result (and possibly set the isnull field of the FunctionCallInfoBaseData structure, if it wishes to return an SQL null result). The difference between a call handler and an ordinary callee function is that the flinfo->fn_oid field of the FunctionCallInfoBaseData structure will contain the OID of the actual function to be called, not of the call handler itself. The call handler must use this field to determine which function to execute. Also, the passed argument list has been set up according to the declaration of the target function, not of the call handler.

It's up to the call handler to fetch the entry of the function from the pg_proc system catalog and to analyze the argument and return types of the called function. The AS clause from the CREATE FUNCTION command for the function will be found in the prosrc column of the pg_proc row. This is commonly source text in the procedural language, but in theory it could be something else, such as a path name to a file, or anything else that tells the call handler what to do in detail.

Often, the same function is called many times per SQL statement. A call handler can avoid repeated lookups of information about the called function by using the flinfo->fn_extra field. This will initially be NULL , but can be set by the call handler to point at information about the called function. On subsequent calls, if flinfo->fn_extra is already non- NULL then it can be used and the information lookup step skipped. The call handler must make sure that flinfo->fn_extra is made to point at memory that will live at least until the end of the current query, since an FmgrInfo data structure could be kept that long. One way to do this is to allocate the extra data in the memory context specified by flinfo->fn_mcxt ; such data will normally have the same lifespan as the FmgrInfo itself. But the handler could also choose to use a longer-lived memory context so that it can cache function definition information across queries.

When a procedural-language function is invoked as a trigger, no arguments are passed in the usual way, but the FunctionCallInfoBaseData 's context field points at a TriggerData structure, rather than being NULL as it is in a plain function call. A language handler should provide mechanisms for procedural-language functions to get at the trigger information.

A template for a procedural-language handler written as a C extension is provided in src/test/modules/plsample . This is a working sample demonstrating one way to create a procedural-language handler, process parameters, and return a value.

Although providing a call handler is sufficient to create a minimal procedural language, there are two other functions that can optionally be provided to make the language more convenient to use. These are a validator and an inline handler . A validator can be provided to allow language-specific checking to be done during CREATE FUNCTION . An inline handler can be provided to allow the language to support anonymous code blocks executed via the DO command.

If a validator is provided by a procedural language, it must be declared as a function taking a single parameter of type oid . The validator's result is ignored, so it is customarily declared to return void . The validator will be called at the end of a CREATE FUNCTION command that has created or updated a function written in the procedural language. The passed-in OID is the OID of the function's pg_proc row. The validator must fetch this row in the usual way, and do whatever checking is appropriate. First, call CheckFunctionValidatorAccess() to diagnose explicit calls to the validator that the user could not achieve through CREATE FUNCTION . Typical checks then include verifying that the function's argument and result types are supported by the language, and that the function's body is syntactically correct in the language. If the validator finds the function to be okay, it should just return. If it finds an error, it should report that via the normal ereport() error reporting mechanism. Throwing an error will force a transaction rollback and thus prevent the incorrect function definition from being committed.

Validator functions should typically honor the check_function_bodies parameter: if it is turned off then any expensive or context-sensitive checking should be skipped. If the language provides for code execution at compilation time, the validator must suppress checks that would induce such execution. In particular, this parameter is turned off by pg_dump so that it can load procedural language functions without worrying about side effects or dependencies of the function bodies on other database objects. (Because of this requirement, the call handler should avoid assuming that the validator has fully checked the function. The point of having a validator is not to let the call handler omit checks, but to notify the user immediately if there are obvious errors in a CREATE FUNCTION command.) While the choice of exactly what to check is mostly left to the discretion of the validator function, note that the core CREATE FUNCTION code only executes SET clauses attached to a function when check_function_bodies is on. Therefore, checks whose results might be affected by GUC parameters definitely should be skipped when check_function_bodies is off, to avoid false failures when restoring a dump.

If an inline handler is provided by a procedural language, it must be declared as a function taking a single parameter of type internal . The inline handler's result is ignored, so it is customarily declared to return void . The inline handler will be called when a DO statement is executed specifying the procedural language. The parameter actually passed is a pointer to an InlineCodeBlock struct, which contains information about the DO statement's parameters, in particular the text of the anonymous code block to be executed. The inline handler should execute this code and return.

It's recommended that you wrap all these function declarations, as well as the CREATE LANGUAGE command itself, into an extension so that a simple CREATE EXTENSION command is sufficient to install the language. See Section 36.17 for information about writing extensions.

The procedural languages included in the standard distribution are good references when trying to write your own language handler. Look into the src/pl subdirectory of the source tree. The CREATE LANGUAGE reference page also has some useful details.


  1. Difference Between Spoken and Written Language

    on written language

  2. The History of Written Language

    on written language

  3. The Written Language

    on written language

  4. Spoken and written language

    on written language

  5. Difference Between Spoken and Written Language

    on written language

  6. PPT

    on written language


  1. Difference b/w Written & Unwritten Constitution #upsc #teluguupsc #upscpolitymcq

  2. Montessori

  3. The Oldest Language Debate

  4. More on Read Write Type

  5. Basic English Writing #1

  6. Virtual Harper Lecture: The Russian Language Empire, with Lenore A. Grenoble


  1. Written language

    Written language. A Specimen of typeset fonts and languages, by William Caslon, letter founder; from the 1728 Cyclopaedia. A written language is the representation of a language by means of writing. This involves the use of visual symbols, known as graphemes, to represent linguistic units such as phonemes, syllables, morphemes, or words.

  2. Language

    Language - Writing, Grammar, Communication: Historically, culturally, and in the individual's life, writing is subsequent to speech or signing and presupposes it. Aristotle expressed the relation thus: "Speech is the representation of the experiences of the mind, and writing is the representation of speech" (On Interpretation). But it is not as simple as this would suggest.

  3. Language

    Language, a system of conventional spoken, manual (signed), or written symbols by means of which human beings express themselves. The functions of language include communication, the expression of identity, play, imaginative expression, and emotional release.

  4. Writing

    Writing is the physical manifestation of a spoken language. It is thought that human beings developed language c. 35,000 BCE as evidenced by cave paintings from the period of the Cro-Magnon Man (c. 50,000-30,000 BCE) which appear to express concepts concerning daily life. These images suggest a language because, in some instances, they seem to ...

  5. 13.1 Oral versus Written Language

    Public speaking, on the other hand, should sound like a conversation. McCroskey, Wrench, and Richmond highlighted the following twelve differences that exist between oral and written language: Oral language has a smaller variety of words. Oral language has words with fewer syllables. Oral language has shorter sentences.

  6. Language

    Language is a structured system of communication that consists of grammar and vocabulary. It is the primary means by which humans convey meaning, both in spoken and written forms, and may also be conveyed through sign languages. Human language is characterized by its cultural and historical diversity, with significant variations observed ...

  7. Written Language

    Definition. Written language is the written form of communication which includes both reading and writing. Although written language may at first be considered to simply be oral language in its written form, the two are quite different in that oral language rules are innate whereas written language is acquired through explicit education.

  8. On The Differences Between Spoken and Written Language

    Drawing on research studies in (socio)linguistics, discourse analysis, and literacy, this paper provides a synthesis of findings about lexical and syntactico-semantic differences between spokken and written language, focusing on empirical research on the English language since the 1920s. The major theoretical and methodological aproaches used ...

  9. Omniglot

    A guide to writing systems and languages, with useful phrases, tips on learning languages, multilingual texts, and much more.

  10. Language Learning Through Writing: Theoretical Perspectives and

    This chapter contributes a review of theoretical perspectives and selected empirical studies on how and why writing can be a site for language learning. This area of scholarly interest, a newcomer to language learning studies, has been characterized as "a well-defined space for a future research domain at the intersection between L2 [second ...

  11. Script

    Script is the written expression of a language.Cuneiform, the first script, was invented in Sumer, Mesopotamia c. 3500 BCE, hieroglyphics sometime prior to the Early Dynastic Period in Egypt (c. 3150-2613 BCE), and Sanskrit in India during the Vedic Period (c. 1500 to c. 500 BCE). Writing was later adopted by other cultures enabling the development of civilization.

  12. History of writing

    The history of writing traces the development of writing systems and how their use transformed and was transformed by different societies. The use of writing prefigures various social and psychological consequences associated with literacy and literary culture.. With each historical invention of writing, true writing systems were preceded by systems of ideographic and mnemonic symbols called ...

  13. The power of language: How words shape people, culture

    Speaking, writing and reading are integral to everyday life, where language is the primary tool for expression and communication. Studying how people use language - what words and phrases they ...

  14. Writing

    Writing - Scripts, Alphabets, Cuneiform: While spoken or signed language is a more or less universal human competence that has been characteristic of the species from the beginning and that is commonly acquired by human beings without systematic instruction, writing is a technology of relatively recent history that must be taught to each generation of children.

  15. What's the World's Oldest Language?

    Although the earliest written evidence of these languages dates back only around 3,000 years, Hieber says that both belong to the Afroasiatic language family, whose roots trace back to 18,000 to ...

  16. What Is a Written Language? (with pictures)

    Written language refers to a language that is written down and used for recording events, ideas and feelings. The opposite of written language is spoken language and there are a number of differences between the two. Accessing and exploiting the written word requires two key language skills: writing and reading. Without these two, especially ...

  17. written language News, Research and Analysis

    Andreea S. Calude, University of Waikato. Spoken language evolves differently and faster than written language, and there are good reasons why this is the case. Browse written language news ...

  18. Writing systems by language

    The most widely used writing systems are the Latin, Cyrillic and Arabic alphabets. An index of all the languages of all the languages featured on this site is available in the language index . Some languages have been written with a number of different writing systems over the years. For example, in Central Asia many languages were originally ...

  19. Language: Its Origin and Ongoing Evolution

    Similarly, for Saussure, written language was an object of suspicion, presenting a confounding and contaminating influence on language, going so far as to state that "to let go of the letter means a first step in the direction of truth" (Saussure et al. 1986, p. 32).

  20. Written Language Disorders

    A disorder of written language involves a significant impairment in fluent word reading (i.e., reading decoding and sight word recognition), reading comprehension, written spelling, and/or written expression (Ehri, 2000; Gough & Tunmer, 1986; Kamhi & Catts, 2012; Tunmer & Chapman, 2007, 2012). A word reading disorder is also known as dyslexia.

  21. Indices of oral and written narratives differentiating Mandarin

    Petersen DB, Staskowski M, Spencer TD, et al. (2022) The effects of a multitiered system of language support on kindergarten oral and written language: A large-scale randomized controlled trial. Language, Speech, and Hearing Services in Schools 53(1): 44-68.

  22. What is Natural Language Processing? Definition and Examples

    Natural language processing (NLP) is a form of artificial intelligence that allows computers to understand human language, whether it be written, spoken, or even scribbled.As AI-powered devices and services become increasingly more intertwined with our daily lives and world, so too does the impact that NLP has on ensuring a seamless human-computer experience.

  23. Language

    Language - Communication, Grammar, Culture: It has been seen that language is much more than the external expression and communication of internal thoughts formulated independently of their verbalization. In demonstrating the inadequacy and inappropriateness of such a view of language, attention has already been drawn to the ways in which one's native language is intimately and in all sorts ...

  24. How to Identify and Translate the Language of any Text?

    Step 1: Open Google, and click on the Camera icon. Step 2: Now, upload a image or take a photo of an image you can to translate.. Step 3: Select and copy the text from the image and click translate button. Step 4: In the next window, you will see the detected language in the left panel and can select a language to translate your text from the ...

  25. Map of Languages Spoken In China

    Here's a detailed look at the writing systems for various languages in China: Languages Using Chinese Characters: Mandarin (Putonghua): Written in Simplified Chinese characters. Cantonese (Yue): Written in Traditional Chinese characters, especially in Hong Kong and Macau, although Simplified characters are used in Mainland China.

  26. Origin of language

    The origin of language, its relationship with human evolution, and its consequences have been subjects of study for centuries.Scholars wishing to study the origins of language must draw inferences from evidence such as the fossil record, archaeological evidence, contemporary language diversity, studies of language acquisition, and comparisons between human language and systems of animal ...

  27. Deal written on a napkin that sent Lionel Messi to Barcelona sells at

    The contract language, written in blue ink, was intended to reassure the teenager's father, Jorge Messi, that the deal would go through. Lionel Messi of Barcelona on April 6, 2010 in Barcelona ...

  28. AI language translation startup DeepL nabs $300M on a $2B valuation to

    DeepL, which builds automated text translation and writing tools that compete against the likes of Google Translate and Grammarly, said on Wednesday that it has raised an additional $300 million ...

  29. Chapter 56. Writing a Procedural Language Handler

    A language handler should provide mechanisms for procedural-language functions to get at the trigger information. A template for a procedural-language handler written as a C extension is provided in src/test/modules/plsample. This is a working sample demonstrating one way to create a procedural-language handler, process parameters, and return a ...

  30. List of languages by first written account

    notes by Johann Flierl, Wilhelm Poland and Georg Schwarz, culminating in Walter Roth 's The Structure of the Koko Yimidir Language in 1901. [181] [182] A list of 61 words recorded in 1770 by James Cook and Joseph Banks was the first written record of an Australian language. [183] c. 1891. Galela.