In 2025, there were around 1.53 billion people worldwide who spoke English either natively or as a second language, slightly more than the 1.18 billion Mandarin Chinese speakers at the time of survey. Hindi and Spanish accounted for the third and fourth most widespread languages that year. Languages in the United States The United States does not have an official language, but the country uses English, specifically American English, for legislation, regulation, and other official pronouncements. The United States is a land of immigration, and the languages spoken in the United States vary as a result of the multicultural population. The second most common language spoken in the United States is Spanish or Spanish Creole, which over than 43 million people spoke at home in 2023. There were also 3.5 million Chinese speakers (including both Mandarin and Cantonese),1.8 million Tagalog speakers, and 1.57 million Vietnamese speakers counted in the United States that year. Different languages at home The percentage of people in the United States speaking a language other than English at home varies from state to state. The state with the highest percentage of population speaking a language other than English is California. About 45 percent of its population was speaking a language other than English at home in 2023.

As of February 2025, English was the most popular language for web content, with over 49.4 percent of websites using it. Spanish ranked second, with six percent of web content, while the content in the German language followed, with 5.6 percent. English as the leading online language United States and India, the countries with the most internet users after China, are also the world's biggest English-speaking markets. The internet user base in both countries combined, as of January 2023, was over a billion individuals. This has led to most of the online information being created in English. Consequently, even those who are not native speakers may use it for convenience. Global internet usage by regions As of October 2024, the number of internet users worldwide was 5.52 billion. In the same period, Northern Europe and North America were leading in terms of internet penetration rates worldwide, with around 97 percent of its populations accessing the internet.

As of 2024, JavaScript and HTML/CSS were the most commonly used programming languages among software developers around the world, with more than 62 percent of respondents stating that they used JavaScript and just around 53 percent using HTML/CSS. Python, SQL, and TypeScript rounded out the top five most widely used programming languages around the world. Programming languages At a very basic level, programming languages serve as sets of instructions that direct computers on how to behave and carry out tasks. Thanks to the increased prevalence of, and reliance on, computers and electronic devices in today’s society, these languages play a crucial role in the everyday lives of people around the world. An increasing number of people are interested in furthering their understanding of these tools through courses and bootcamps, while current developers are constantly seeking new languages and resources to learn to add to their skills. Furthermore, programming knowledge is becoming an important skill to possess within various industries throughout the business world. Job seekers with skills in Python, R, and SQL will find their knowledge to be among the most highly desirable data science skills and likely assist in their search for employment.

In 2023, around 43.37 million people in the United States spoke Spanish at home. In comparison, approximately 998,179 people were speaking Russian at home during the same year. The distribution of the U.S. population by ethnicity can be accessed here. A ranking of the most spoken languages across the world can be accessed here.

As of 2023, more than 22 percent of people in the United States spoke a language other than English at home. California had the highest share among all U.S. states, with 45 percent of its population speaking a language other than English at home.

The GlobalPhone corpus developed in collaboration with the Karlsruhe Institute of Technology (KIT) was designed to provide read speech data for the development and evaluation of large continuous speech recognition systems in the most widespread languages of the world, and to provide a uniform, multilingual speech and text database for language independent and language adaptive speech recognition as well as for language identification tasks. The entire GlobalPhone corpus enables the acquisition of acoustic-phonetic knowledge of the following 22 spoken languages: Arabic (ELRA-S0192), Bulgarian (ELRA-S0319), Chinese-Mandarin (ELRA-S0193), Chinese-Shanghai (ELRA-S0194), Croatian (ELRA-S0195), Czech (ELRA-S0196), French (ELRA-S0197), German (ELRA-S0198), Hausa (ELRA-S0347), Japanese (ELRA-S0199), Korean (ELRA-S0200), Polish (ELRA-S0320), Portuguese (Brazilian) (ELRA-S0201), Russian (ELRA-S0202), Spanish (Latin America) (ELRA-S0203), Swahili (ELRA-S0375), Swedish (ELRA-S0204), Tamil (ELRA-S0205), Thai (ELRA-S0321), Turkish (ELRA-S0206), Ukrainian (ELRA-S0377), and Vietnamese (ELRA-S0322).In each language about 100 sentences were read from each of the 100 speakers. The read texts were selected from national newspapers available via Internet to provide a large vocabulary. The read articles cover national and international political news as well as economic news. The speech is available in 16bit, 16kHz mono quality, recorded with a close-speaking microphone (Sennheiser 440-6). The transcriptions are internally validated and supplemented by special markers for spontaneous effects like stuttering, false starts, and non-verbal effects like laughing and hesitations. Speaker information like age, gender, occupation, etc. as well as information about the recording setup complement the database. The entire GlobalPhone corpus contains over 450 hours of speech spoken by more than 2100 native adult speakers.Data is shortened by means of the shorten program written by Tony Robinson. Alternatively, the data could be delivered unshorten.The Chinese-Mandarin corpus was produced using the Peoples Daily newspaper. It contains recordings of 132 speakers (64 males, 68 females) recorded in Beijing, Wuhan and Hekou, China. The following age distribution has been obtained: 16 speakers are below 19, 96 speakers are between 20 and 29, 16 speakers are between 30 and 39, 3 speakers are between 40 and 49 (1 speaker age is unknown).

Mexico is the country with the largest number of native Spanish speakers in the world. As of 2024, 132.5 million people in Mexico spoke Spanish with a native command of the language. Colombia was the nation with the second-highest number of native Spanish speakers, at around 52.7 million. Spain came in third, with 48 million, and Argentina fourth, with 46 million. Spanish, a world language As of 2023, Spanish ranked as the fourth most spoken language in the world, only behind English, Chinese, and Hindi, with over half a billion speakers. Spanish is the official language of over 20 countries, the majority on the American continent, nonetheless, it's also one of the official languages of Equatorial Guinea in Africa. Other countries have a strong influence, like the United States, Morocco, or Brazil, countries included in the list of non-Hispanic countries with the highest number of Spanish speakers. The second most spoken language in the U.S. In the most recent data, Spanish ranked as the language, other than English, with the highest number of speakers, with 12 times more speakers as the second place. Which comes to no surprise following the long history of migrations from Latin American countries to the Northern country. Moreover, only during the fiscal year 2022. 5 out of the top 10 countries of origin of naturalized people in the U.S. came from Spanish-speaking countries.

Context:

There are over 7,000 human languages in the world. The World Atlas of Language Structures (WALS) contains information on the structure of 2,679 of them. It also includes information about where languages are used. WALS is widely-cited and used in the linguistics research community.

Content:

The World Atlas of Language Structures (WALS) is a large database of structural (phonological, grammatical, lexical) properties of languages gathered from descriptive materials (such as reference grammars) by a team of 55 authors. The atlas provides information on the location, linguistic affiliation and basic typological features of a great number of the world's languages

WALS Online is a publication of the (Max Planck Institute for Evolutionary Anthropology)[http://www.eva.mpg.de/]. It is a separate publication, edited by Dryer, Matthew S. & Haspelmath, Martin (Leipzig: Max Planck Institute for Evolutionary Anthropology, 2013) The main programmer is Robert Forkel.

This dataset includes three files:

• source.bib: A BibTex file with all of the sources cited in the dataset in it
• language.csv: A file with a list of all the languages included in WALS
• wals-data.csv: A file containing information on the features associated with each individual language

Acknowledgements:

This dataset is licensed under a Creative Commons Attribution 4.0 International License .

The World Atlas of Language Structures was edited by Matthew Dryer and Martin Haspelmath. If you use this data in your work, please include the following citation:

Dryer, Matthew S. & Haspelmath, Martin (eds.) 2013. The World Atlas of Language Structures Online. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at http://wals.info, Accessed on September 7, 2017.)

Inspiration:

• This dataset was designed to make interactive maps of language features. Can you make an interactive map that shows different linguistic features? You might find it helpful to use Leaflet (for R) or Plotly (for Python). This blog post is a great resource to help you get started.
• There’s a lot of discussion of “linguistic universals” in linguistics. These are specific features that every language (should) have. Can you identify any features that you think may be universals from this dataset?

You may also like:

• Atlas of Pidgin and Creole Language Structures: Information on 76 Creole and Pidgin Languages
• World Language Family Map
• The Sign Language Analyses (SLAY) Database

Language Learning Software Market Outlook

The global language learning software market size in 2023 is estimated to be approximately USD 14.5 billion and is projected to grow significantly, reaching USD 33.5 billion by 2032, with a compound annual growth rate (CAGR) of 9.5% from 2024 to 2032. The market's robust expansion is primarily driven by the growing demand for multilingual proficiency in an increasingly globalized world. As businesses operate across borders and cultures, the need for language skills becomes more vital than ever. Digital transformation and technological advancements have further accelerated the adoption of language learning software, providing users with interactive, flexible, and personalized learning experiences.

One of the key growth factors of the language learning software market is the increasing emphasis on language skills in the academic sector. Schools and universities worldwide are integrating digital language learning tools into their curricula to enhance students' linguistic capabilities. This trend is particularly pronounced in regions where English is not the first language, as English has become the lingua franca of international communication. Language learning software offers an effective, engaging, and scalable solution for educational institutions to teach languages, supporting diverse learning styles and paces. Furthermore, the convenience of on-demand access and the ability to track progress are making such software an attractive choice for educators.

Another significant driver of market growth is the rise of corporate training programs focused on enhancing employees' language skills. As organizations expand globally, bridging language barriers becomes crucial for successful operations, negotiations, and customer interactions. Consequently, businesses are investing in language learning software to train their workforce. The software allows companies to provide uniform language training across diverse geographic locations, ensuring that employees are equipped with the necessary skills to communicate effectively with international clients and colleagues. This corporate demand is further fueled by the software's ability to offer tailored learning paths and real-time performance analytics, thus maximizing the return on investment.

The proliferation of smart devices and increasing internet penetration have propelled the popularity of language learning apps, contributing significantly to market growth. Apps offer unparalleled accessibility, enabling users to learn languages at their convenience, whether on the go or at home. This flexibility is particularly appealing to individual learners who are juggling busy schedules. The availability of engaging, gamified content and social learning features in these apps enhances user retention and motivation, further boosting their adoption. Moreover, advancements in artificial intelligence and machine learning are enabling more sophisticated and personalized learning experiences, driving continued market expansion.

Regionally, the Asia Pacific market is expected to exhibit the highest growth rate over the forecast period, driven by the increasing importance of English in business and education sectors. Countries like China, India, and Japan are witnessing a surge in demand for English language learning solutions, fueled by globalization and competitive academic and corporate landscapes. North America, being home to some of the largest providers of language learning software, holds a significant market share. However, Europe also presents promising growth opportunities, particularly due to the diverse linguistic landscape and the emphasis on multilingualism in education and business. The Middle East & Africa and Latin America are gradually recognizing the benefits of language proficiency, contributing to the global market growth.

Product Type Analysis

The language learning software market is segmented by product type into self-paced e-learning, online tutoring, apps-based learning, and others, each offering unique advantages and catering to different learning preferences. Self-paced e-learning remains one of the most popular segments, offering learners the flexibility to access course materials and content at their convenience. This mode is particularly suitable for individuals who prefer to learn at their own pace, without the constraints of a fixed schedule. The asynchronous nature of self-paced e-learning allows learners to revisit challenging concepts, ensuring a comprehensive understanding before progressing. This segment is also favored for its cost-effectiveness and the breadth of courses availabl

Abstract. English has long been set as the “universal language.” Basically most, if not all countries in the world know how to speak English or at least try to use it in their everyday communications for the purpose of globalizing. This study is designed to help the students learn from one or all of the four most commonly used foreign languages in the field of Information Technology namely; Korean, Mandarin Chinese, Japanese, and Spanish. Composed of a set of words, phrases, and sentences, the program is intended to quickly teach the students in the form of basic, intermediate, and advanced levels. This study has used the Agile model in system development. Functionality, reliability, usability, efficiency, and portability were also considered in determining the level of the system’s acceptability in terms of ISO 25010:2011. This interactive foreign language trainer is built to associate fun with learning, to remedy the lack of perseverance by some in learning a new language, and to make learning the user’s favorite playtime activity. The study allows the user to interact with the program which provides support for their learning. Moreover, this study reveals that integrating feedback modalities in the training and assessment modules of the software strengthens and enhances the memory in learning the language.

Keywords: Feedback, Assessment, Learning Modalities, Language Trainer, Interactive Technology

The GlobalPhone corpus developed in collaboration with the Karlsruhe Institute of Technology (KIT) was designed to provide read speech data for the development and evaluation of large continuous speech recognition systems in the most widespread languages of the world, and to provide a uniform, multilingual speech and text database for language independent and language adaptive speech recognition as well as for language identification tasks. The entire GlobalPhone corpus enables the acquisition of acoustic-phonetic knowledge of the following 22 spoken languages: Arabic (ELRA-S0192), Bulgarian (ELRA-S0319), Chinese-Mandarin (ELRA-S0193), Chinese-Shanghai (ELRA-S0194), Croatian (ELRA-S0195), Czech (ELRA-S0196), French (ELRA-S0197), German (ELRA-S0198), Hausa (ELRA-S0347), Japanese (ELRA-S0199), Korean (ELRA-S0200), Polish (ELRA-S0320), Portuguese (Brazilian) (ELRA-S0201), Russian (ELRA-S0202), Spanish (Latin America) (ELRA-S0203), Swahili (ELRA-S0375), Swedish (ELRA-S0204), Tamil (ELRA-S0205), Thai (ELRA-S0321), Turkish (ELRA-S0206), Ukrainian (ELRA-S0377), and Vietnamese (ELRA-S0322).In each language about 100 sentences were read from each of the 100 speakers. The read texts were selected from national newspapers available via Internet to provide a large vocabulary. The read articles cover national and international political news as well as economic news. The speech is available in 16bit, 16kHz mono quality, recorded with a close-speaking microphone (Sennheiser 440-6). The transcriptions are internally validated and supplemented by special markers for spontaneous effects like stuttering, false starts, and non-verbal effects like laughing and hesitations. Speaker information like age, gender, occupation, etc. as well as information about the recording setup complement the database. The entire GlobalPhone corpus contains over 450 hours of speech spoken by more than 2100 native adult speakers.Data is shortened by means of the shorten program written by Tony Robinson. Alternatively, the data could be delivered unshorten.The Swedish corpus was produced using the Goeteborgs-Posten newspaper. It contains recordings of 98 speakers (50 males, 48 females) recorded in Stockholm and Vaernamo, Sweden. The following age distribution has been obtained: 9 speakers are below 19, 50 speakers are between 20 and 29, 12 speakers are between 30 and 39, 11 speakers are between 40 and 49, and 16 speakers are over 50.

Advancements in deep learning have revolutionized numerous real-world applications, including image recognition, visual question answering, and image captioning. Among these, image captioning has emerged as a critical area of research, with substantial progress achieved in Arabic, Chinese, Uyghur, Hindi, and predominantly English. However, despite Urdu being a morphologically rich and widely spoken language, research in Urdu image captioning remains underexplored due to a lack of resources. This study creates a new Urdu Image Captioning Dataset (UCID) called UC-23-RY to fill in the gaps in Urdu image captioning. The Flickr30k dataset inspired the 159,816 Urdu captions in the dataset. Additionally, it suggests deep learning architectures designed especially for Urdu image captioning, including NASNetLarge-LSTM and ResNet-50-LSTM. The NASNetLarge-LSTM and ResNet-50-LSTM models achieved notable BLEU-1 scores of 0.86 and 0.84 respectively, as demonstrated through evaluation in this study accessing the model’s impact on caption quality. Additionally, it provides useful datasets and shows how well-suited sophisticated deep learning models are for improving automatic Urdu image captioning.

Native Language Dubbing Market Outlook

The global market size for native language dubbing was valued at approximately USD 3.4 billion in 2023 and is projected to reach USD 6.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. The substantial growth of this market is driven by the increasing demand for localized content across various media platforms, alongside the rising penetration of online streaming services worldwide.

One of the key growth factors for the native language dubbing market is the surge in the consumption of multimedia content. As globalization increases, audiences prefer content in their native languages to enhance the viewing experience. This trend is particularly evident in non-English speaking countries where localized content garners more substantial viewership, thereby driving the demand for dubbing services. Furthermore, the advent of advanced dubbing technologies has made it easier and more cost-effective to produce high-quality dubbed content, thereby propelling market growth.

Another significant factor contributing to the market's expansion is the growth of the entertainment industry. With more international movies and TV shows being produced, there is an increased need to make this content accessible to diverse linguistic audiences. The entertainment industry, therefore, greatly benefits from native language dubbing services, enabling it to reach broader audiences. Additionally, the rise of global media conglomerates and international co-productions further fuels the need for dubbing services, as these entities aim to cater to a global audience with varied linguistic preferences.

The proliferation of online streaming platforms such as Netflix, Amazon Prime, and Disney+ is another critical driver of the native language dubbing market. These platforms have revolutionized content consumption patterns, offering a multitude of international content that requires localization for different regions. The competitive edge of these streaming platforms often lies in their ability to provide a personalized viewing experience, which includes offering content in the native language of the viewer. This trend is expected to continue, thereby significantly boosting the demand for dubbing services.

Regionally, the Asia Pacific market is anticipated to witness the fastest growth, attributed to its large and diverse linguistic population. Countries like India and China, with their numerous regional languages, present a vast market for native language dubbing services. North America and Europe also hold substantial market shares due to the presence of established entertainment industries and high consumer spending on media and entertainment. The increased focus on content localization in these regions further supports market growth. Meanwhile, Latin America and the Middle East & Africa are emerging markets with promising growth potential, driven by the increasing popularity of international content and the need for localization.

Content Type Analysis

In the native language dubbing market, content type plays a pivotal role, with movies, TV shows, documentaries, advertisements, and others being the primary segments. Movies represent a significant portion of the market due to the global nature of the film industry. Major film studios are increasingly focusing on international releases, necessitating high-quality dubbing to cater to non-English speaking audiences. This trend is particularly strong in countries with large film industries, such as India and China, where regional language films dominate the box office. Additionally, the availability of dubbed movies on streaming platforms has further amplified the demand for dubbing services.

TV shows are another critical segment, primarily driven by the rise of international television series that are gaining popularity across different regions. The success of non-native language TV shows hinges on effective dubbing, which allows viewers to enjoy the content without language barriers. Streaming services have also played a crucial role here, offering extensive libraries of dubbed TV shows that cater to a global audience. As these platforms continue to expand their reach, the demand for dubbing TV shows in various languages is expected to grow substantially.

Documentaries, although a smaller segment, are experiencing steady growth in the native language dubbing market. Documentaries often cover culturally significant topics and are intended for educational purposes, making them highly relevant to local audiences. Du

JavaScript and Java were some of the most tested programming languages on the DevSkiller platform as of 2024. SQL and Python ranked second and fourth, with 15 percent and 11 percent of respondents testing this language in 2024, respectively. Nevertheless, the tech skill developers wanted to learn the most in 2024 was related to artificial intelligence, machine learning, and deep learning. At the same time, the fastest growing IT skills among DevSkiller customers were C/C++ and data science, while cybersecurity ranked third. Software skills When it came to the most used programming language among developers worldwide, JavaScript took the top spot, chosen by 62 percent of surveyed respondents. Most software developers learn how to code between 11 and 17 years old, with some of them writing their first line of code by the age of five. Moreover, seven out of 10 developers learned how to program by accessing online resources such as videos and blogs. Software skills pay In 2024, the average annual software developer’s salary in the U.S. amounted to nearly 77 thousand U.S. dollars, while in Germany, it totaled above 54 thousand U.S. dollars. The programming languages associated with the highest salaries worldwide in 2024 were Clojure and Erlang.

KUMono (Khulna University Monolingual corpus).

Snapshot img

Language Translation Software Market Size 2024-2028

The language translation software market size is forecast to increase by USD 8.9 billion at a CAGR of 14.7% between 2023 and 2028.

The market is experiencing significant growth due to the increasing need for seamless communication across languages. Two primary categories driving this market are the translation of text and speech. Advanced algorithms and language databases enable accurate translation between source and target languages. The market is witnessing the implementation of artificial intelligence, natural language processing, and enhanced AI algorithms to improve contextual understanding and provide precise translations. Machine translation and computer-assisted translation are popular approaches, with solutions like Google Translate leading the automatic translation trend. However, open-source software providers pose a threat with their free offerings. To stay competitive, market players must focus on enhancing translation quality and expanding language support. Additionally, cloud-based solutions are gaining traction due to their accessibility and cost-effectiveness. Overall, the market is an essential tool for businesses seeking to expand globally and foster cross-cultural communication.

What will be the Size of the Market During the Forecast Period?

Request Free Sample

The market is witnessing significant growth due to the increasing demand for multilingual content in business communication. With the prevalence of globalization and international trade, companies are expanding their reach to diverse markets, necessitating the need for effective language translation solutions. Businesses require high-quality translations to communicate with clients, partners, and employees in different parts of the world. Translation software plays a crucial role in facilitating seamless communication by translating text and speech in real-time. The software uses advanced algorithms, statistical models, neural networks, and rule-based methods to deliver quick and accurate translations.



The translation process involves several stages, including text analysis, machine translation, and post-editing by human translators. Translation memory and terminology databases are essential features that ensure consistency and accuracy in translations. Real-time collaboration enables multiple users to work on a project simultaneously, enhancing productivity and efficiency. Language translation software supports various languages, including complex texts and idiomatic expressions. The software can translate text and speech, making it an indispensable tool for businesses that operate in multiple languages. The user experience is another critical factor that influences the adoption of language translation software. Natural-sounding voice translations and the ability to handle complex sentences are essential features that enhance the user experience.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Solution

  Rule-based machine translation
  Statistical-based machine translation
  Hybrid machine translation
  Others


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Solution Insights

The rule-based machine translation segment is estimated to witness significant growth during the forecast period.

Machine translation software enables the conversion of text or speech from one language to another. Two primary types of machine translation exist: rule-based and statistical. Rule-based machine translation (RBMT) employs linguistic knowledge about source and target languages to generate translations. Unlike statistical machine translation, RBMT does not rely on bilingual corpora for creating translation systems. Instead, human experts design and develop rules based on their understanding of the language's grammar and syntax. RBMT's rule-based engines generate translations using linguistic rules, making them suitable for creating grammar programs and dictionaries. These rules can be refined and edited by users to enhance translation accuracy.

RBMT's translation lexicons are manually developed, allowing for more precise translations of specific terminology. Google Translate and other automatic translation tools employ statistical machine translation, which uses large bilingual corpora to learn patterns and generate translations. While statistical models offer faster translation speeds, they may not always produce accurate results, especially for technical or specialized terminology. Professionals in the localization industry often use computer

The GlobalPhone corpus developed in collaboration with the Karlsruhe Institute of Technology (KIT) was designed to provide read speech data for the development and evaluation of large continuous speech recognition systems in the most widespread languages of the world, and to provide a uniform, multilingual speech and text database for language independent and language adaptive speech recognition as well as for language identification tasks. The entire GlobalPhone corpus enables the acquisition of acoustic-phonetic knowledge of the following 22 spoken languages: Arabic (ELRA-S0192), Bulgarian (ELRA-S0319), Chinese-Mandarin (ELRA-S0193), Chinese-Shanghai (ELRA-S0194), Croatian (ELRA-S0195), Czech (ELRA-S0196), French (ELRA-S0197), German (ELRA-S0198), Hausa (ELRA-S0347), Japanese (ELRA-S0199), Korean (ELRA-S0200), Polish (ELRA-S0320), Portuguese (Brazilian) (ELRA-S0201), Russian (ELRA-S0202), Spanish (Latin America) (ELRA-S0203), Swahili (ELRA-S0375), Swedish (ELRA-S0204), Tamil (ELRA-S0205), Thai (ELRA-S0321), Turkish (ELRA-S0206), Ukrainian (ELRA-S0377), and Vietnamese (ELRA-S0322).In each language about 100 sentences were read from each of the 100 speakers. The read texts were selected from national newspapers available via Internet to provide a large vocabulary. The read articles cover national and international political news as well as economic news. The speech is available in 16bit, 16kHz mono quality, recorded with a close-speaking microphone (Sennheiser 440-6). The transcriptions are internally validated and supplemented by special markers for spontaneous effects like stuttering, false starts, and non-verbal effects like laughing and hesitations. Speaker information like age, gender, occupation, etc. as well as information about the recording setup complement the database. The entire GlobalPhone corpus contains over 450 hours of speech spoken by more than 2100 native adult speakers.Data is shortened by means of the shorten program written by Tony Robinson. Alternatively, the data could be delivered unshorten.The Portuguese (Brazilian) corpus was produced using the Folha de Sao Paulo newspaper. It contains recordings of 102 speakers (54 males, 48 females) recorded in Porto Velho and Sao Paulo, Brazil. The following age distribution has been obtained: 6 speakers are below 19, 58 speakers are between 20 and 29, 27 speakers are between 30 and 39, 5 speakers are between 40 and 49, and 5 speakers are over 50 (1 speaker age is unknown).

The GlobalPhone corpus developed in collaboration with the Karlsruhe Institute of Technology (KIT) was designed to provide read speech data for the development and evaluation of large continuous speech recognition systems in the most widespread languages of the world, and to provide a uniform, multilingual speech and text database for language independent and language adaptive speech recognition as well as for language identification tasks.

The entire GlobalPhone corpus enables the acquisition of acoustic-phonetic knowledge of the following 22 spoken languages: Arabic (ELRA-S0192), Bulgarian (ELRA-S0319), Chinese-Mandarin (ELRA-S0193), Chinese-Shanghai (ELRA-S0194), Croatian (ELRA-S0195), Czech (ELRA-S0196), French (ELRA-S0197), German (ELRA-S0198), Hausa (ELRA-S0347), Japanese (ELRA-S0199), Korean (ELRA-S0200), Polish (ELRA-S0320), Portuguese (Brazilian) (ELRA-S0201), Russian (ELRA-S0202), Spanish (Latin America) (ELRA-S0203), Swahili (ELRA-S0375), Swedish (ELRA-S0204), Tamil (ELRA-S0205), Thai (ELRA-S0321), Turkish (ELRA-S0206), Ukrainian (ELRA-S0377), and Vietnamese (ELRA-S0322).

In each language about 100 sentences were read from each of the 100 speakers. The read texts were selected from national newspapers available via Internet to provide a large vocabulary. The read articles cover national and international political news as well as economic news. The speech is available in 16bit, 16kHz mono quality, recorded with a close-speaking microphone (Sennheiser 440-6). The transcriptions are internally validated and supplemented by special markers for spontaneous effects like stuttering, false starts, and non-verbal effects like laughing and hesitations. Speaker information like age, gender, occupation, etc. as well as information about the recording setup complement the database. The entire GlobalPhone corpus contains over 450 hours of speech spoken by more than 2100 native adult speakers.

Data is shortened by means of the shorten program written by Tony Robinson. Alternatively, the data could be delivered unshorten.

The Croatian corpus was produced using the HRT and Obzor Nacional newspapers. It contains recordings of 94 speakers (38 males, 56 females) recorded in Zagreb, Croatia, and parts of Bosnia. The following age distribution has been obtained: 21 speakers are below 19, 30 speakers are between 20 and 29, 14 speakers are between 30 and 39, 15 speakers are between 40 and 49, and 13 speakers are over 50 (1 speaker age is unknown).

Papua New Guinea is the most linguistically diverse country in the world. As of 2025, it was home to 840 different languages. Indonesia ranked second with 709 languages spoken. In the United States, 335 languages were spoken in that same year.

Snapshot img

Text-To-Speech Market Size 2025-2029

The text-to-speech market size is forecast to increase by USD 3.99 billion, at a CAGR of 14.1% between 2024 and 2029.

The Text-To-Speech (TTS) market is experiencing significant growth, driven primarily by the increasing demand for voice-enabled devices. This trend is expected to continue as technology advances and voice interfaces become more integrated into daily life. Another key driver is the development of AI-based TTS models, which offer improved accuracy and natural-sounding voices. However, regulatory compliance poses a significant challenge for market players. Technology advancements, such as artificial intelligence and machine learning, are revolutionizing the delivery. As governments and regulatory bodies impose stricter guidelines on data privacy and security, TTS providers must ensure their solutions meet these requirements to maintain customer trust and avoid potential legal issues.
The proliferation of high-speed internet, smartphones, and tablets has further fueled market expansion. Companies seeking to capitalize on market opportunities in the TTS space should focus on developing advanced, AI-driven TTS models while prioritizing regulatory compliance to navigate this complex landscape.

What will be the Size of the Text-To-Speech Market during the forecast period?

Request Free Sample

The text-to-speech (TTS) market is experiencing significant advancements in speech recognition technology and voice search optimization. Metrics such as speech recognition dataset, voice modulation, and voice cloning play a crucial role in evaluating TTS systems' performance. Speech synthesis evaluation and voice cloning evaluation are essential for ensuring high-quality audiobook narration and call center automation. Voice modulation technology and voice cloning technology are revolutionizing industries like interactive voice response and speech interface design. VPNs and secure platforms are essential to ensure data security. Convolutional neural networks and transformer networks are driving improvements in speech recognition quality and speech synthesis quality. Voice commerce and human-computer interaction are benefiting from these advancements, with voice modulation metrics and speech-to-text metrics playing a key role in voice commerce evaluation.
Audiobook narration and speech-to-text quality are essential for digital signage applications. Vocal training and speech therapy are also utilizing speech-to-text datasets and deep neural networks for data augmentation, enhancing the overall effectiveness of these applications. Voice banking and voice interface design are further expanding the use cases for TTS technology. In summary, the TTS market is witnessing continuous innovation, with advancements in speech recognition, voice modulation, and voice cloning metrics driving improvements in various industries, including call centers, e-commerce, and digital signage.

How is this Text-To-Speech Industry segmented?

The text-to-speech industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Language

  English
  Chinese
  Spanish
  Others


Technology

  Neural TTS
  Concatenative TTS
  Formant-based TTS


Type

  Natural voices
  Synthetic voices


End-user

  Automotive and transportation
  Healthcare
  Consumer Electronics
  Finance
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Language Insights

The english segment is estimated to witness significant growth during the forecast period. The Text-to-Speech (TTS) market is witnessing significant growth, driven by the increasing adoption of English language systems in various sectors. English, as the most widely used language, holds a dominant position in this market due to its extensive application in business, education, media, and technology. TTS solutions for English are developed with a diverse range of voice options, including regional accents such as American, British, and Australian, and multiple speaking styles, from formal and instructional to conversational and expressive. Virtual assistants, customer service platforms, e-learning modules, and accessibility tools are among the major applications of English TTS systems.

The integration of these solutions in these domains reflects both the global reach of the English language and the technological advancements supporting it. Advanced functionalities such as speech recognition, speaker identification, and conversational AI are becoming increasingly common in TTS systems, enhancing their capabilities and usability. Moreover, the integration of TTS t