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.

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.

Find out which are the top 10 most spoken languages in the world according to GeoNames and preserve the data containing the information needed, as some countries get split or merged, some languages get extinct, etc.

Context

After going through quite the verbal loop when ordering foreign currency through the bank, which involved a discussion with an assigned financial advisor at the branch the following day to confirm details, I noticed despite our names hinting at the assumed typical background similarities, communication by phone was much more difficult due to the thickness in accents and different speech patterns when voicing from a non-native speaker.

It hit me then coming from an extremely multicultural and welcoming city, the challenges others from completely different labels given to them in life must go through in their daily affairs when having to face communication barriers that I myself encountered, particularly when interacting with those outside their usual bubble. Now imagine this situation occurring every hour across the world in various sectors of business. How may this impede, help or create frustrations in minor or major ways as a result of increasing workplace diversity quota demands, customer satisfaction needs and process efficiencies?

The data I was looking for to explore this phenomena existed in the form of native and non-native speakers of the 100 most commonly spoken languages across the globe.

Content

The data in this database contains the following attributes:

• Language - name of the language
• Total Speakers - this assumes both native and non-native speakers
• Native Speakers - native speakers of the language
• Origin - family origin group of said language

Acknowledgements

The data was collected with the aid of WordTips visualization of the 22nd edition of Ethnologue - "a research center for language intelligence"

https://www.ethnologue.com/world https://www.ethnologue.com/guides/ethnologue200 https://word.tips/pictures/b684e98f-f512-4ac0-96a4-0efcf6decbc0_most-spoken-languages-world-5.png?auto=compress,format&rect=0,0,2001,7115&w=800&h=2845

Inspiration

As globalization no longer constrains us, what implications will this have in terms of organizational communications conducted moving forward? I believe this is something to be examined in careful context in order to make customer relationship processes meaningful rather than it being confined to a strictly detached transactional basis.

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.

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.

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 Arabic corpus was produced using the Assabah newspaper. It contains recordings of 78 speakers (35 males, 43 females) recorded in Tunisia, Palestine and Jordan. The following age distribution has been obtained: 20 speakers are below 19, 35 speakers are between 20 and 29, 13 speakers are between 30 and 39, 6 speakers are between 40 and 49, and 4 speakers are over 50.

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).

Introduction:

This dataset provides detailed information on the number of words in various languages. It includes a comprehensive list of word counts for multiple languages, making it a valuable resource for linguists, language learners, and anyone interested in language diversity. The dataset is presented in the form of a list of dictionaries, with each dictionary containing information on the language, the number of words, and other details.

The dataset covers a wide range of languages from around the world, including commonly spoken languages like English, Spanish, Mandarin, and Arabic, as well as lesser-known languages. The word counts are approximate and are based on the number of words in the respective dictionaries.

In addition to the word counts, the dataset also includes information on the approximate number of headwords and definitions available for each language. This information provides further insight into the depth and complexity of the vocabulary of each language.

The dataset is useful for a variety of purposes, such as language research, linguistic diversity studies, language teaching and learning, and natural language processing. The data is provided in a machine-readable format, making it easy to use and analyze.

This dataset is a valuable resource for anyone interested in the linguistic diversity of the world's languages and provides a starting point for exploring the vast vocabulary of different languages.

Column Descriptors:

Language: The name of the language the dictionary pertains to. Number of Words: The approximate number of words included in the dictionary. Approx Headwords: The approximate number of headwords included in the dictionary. Approx Definitions: The approximate number of definitions included in the dictionary. Dictionary: The name or type of dictionary included in the dataset. Notes: Any additional notes or information regarding the dictionary or language.

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 first public large-scale multilingual Twitter dataset related to the FIFA World Cup 2022, comprising over 28 million posts in 69 unique spoken languages, including Arabic, English, Spanish, French, and many others. This dataset aims to facilitate research in future sentiment analysis, cross-linguistic studies, event-based analytics, meme and hate speech detection, fake news detection, and social manipulation detection.

The file 🚨Qatar22WC.csv🚨 contains tweet-level and user-level metadata for our collected tweets. 🚀Codebook for FIFA World Cup 2022 Twitter Dataset🚀 | Column Name | Description| |-------------------------------- |----------------------------------------------------------------------------------------| | day, month, year | The date where the tweet posted | | hou, min, sec | Hour, minute, and second of tweet timestamp | | age_of_the_user_account | User Account age in days | | tweet_count | Total number of tweets posted by the user | | location | User-defined location field | | follower_count | Number of followers the user has | | following_count | Number of accounts the user is following | | follower_to_Following | Follower-following ratio | | favouite_count | Number of likes the user did| | verified | Boolean indicating if the user is verified (1 = Verified, 0 = Not Verified) | | Avg_tweet_count | Average tweets per day for the user activity| | list_count | Number of lists the user is a member | | Tweet_Id | Tweet ID | | is_reply_tweet | ID of the tweet being replied to (if applicable) | | is_quote | boolean representing if the tweet is a quote | | retid | Retweet ID if it's a retweet; NaN otherwise | | lang | Language of the tweet | | hashtags | The keyword or hashtag used to collect the tweet | | is_image, | Boolean indicating if the tweet associated with image| | is_video | Boolean indicating if the tweet associated with video | |-------------------------------|----------------------------------------------------------------------------------------|

Examples of use case queries are described in the file 🚨fifa_wc_qatar22_examples_of_use_case_queries.ipynb🚨 and accessible via: https://github.com/khairied/Qata_FIFA_World_Cup_22

🚀 Please Cite This as: Daouadi, K. E., Boualleg, Y., Guehairia, O. & Taleb-Ahmed, A. (2025). Tracking the Global Pulse: The first public Twitter dataset from FIFA World Cup, Journal of Computational Social Science.

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.

Spanish is the second most widely-spoken language on Earth; over one in 20 humans alive today is a native speaker of Spanish. This medium-sized corpus contains 120 million words of modern Spanish taken from the Spanish-Language Wikipedia in 2010. This dataset is made up of 57 text files. Each contains multiple Wikipedia articles in an XML format. The text of each article is surrounded by tags. The initial tag also contains metadata about the article, including the article’s id and the title of the article. The text “ENDOFARTICLE.” appears at the end of each article, before the closing tag.

Dataset Card for "XLingHealth"

XLingHealth is a Cross-Lingual Healthcare benchmark for clinical health inquiry that features the top four most spoken languages in the world: English, Spanish, Chinese, and Hindi.

  Statistics

Dataset

Examples

Words (Q)

Words (A)

HealthQA 1,134 7.72 ± 2.41 242.85 ± 221.88

LiveQA 246 41.76 ± 37.38 115.25 ± 112.75

MedicationQA 690 6.86 ± 2.83 61.50 ± 69.44

Words (Q) and #Words (A) represent the average number of words… See the full description on the dataset page: https://huggingface.co/datasets/claws-lab/XLingHealth.

Does the person speak a language other than English at home? This map takes a look at answers to this question from Census Night.Colour:For each SA1 geography, the colour indicates which language 'wins'.SA1 geographies not coloured are either tied between two languages or not enough data Colour Intensity:The colour intensity compares the values of the winner to all other values and returns its dominance over other languages in the same geographyNotes:Only considers top 6 languages for VICCensus 2016 DataPacksPredominance VisualisationsSource CodeNotice that while one language level appears to dominate certain geographies, it doesn't necessarily mean it represents the majority of the population. In fact, as you explore most areas, you will find the predominant language makes up just a fraction of the population due to the number of languages considered.

As of 2023, more than ** 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 ** percent of its population speaking a language other than English at home.

Dataset Card for [Dataset Name]

  Dataset Summary

JamPatoisNLI provides the first dataset for natural language inference in a creole language, Jamaican Patois. Many of the most-spoken low-resource languages are creoles. These languages commonly have a lexicon derived from a major world language and a distinctive grammar reflecting the languages of the original speakers and the process of language birth by creolization. This gives them a distinctive place in exploring the… See the full description on the dataset page: https://huggingface.co/datasets/Ruth-Ann/jampatoisnli.

BanglaNLP: Bengali-English Parallel Dataset Tools

BanglaNLP is a comprehensive toolkit for creating high-quality Bengali-English parallel datasets from news sources, designed to improve machine translation and other cross-lingual NLP tasks for the Bengali language. Our work addresses the critical shortage of high-quality parallel data for Bengali, the 7th most spoken language in the world with over 230 million speakers.

  🏆 Impact & Recognition

120K+ Sentence Pairs:… See the full description on the dataset page: https://huggingface.co/datasets/likhonsheikh/BanglaNLP.

According to Cognitive Market Research, the global language services market size was estimated at USD 58.9 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 6.2% from 2023 to 2030. Which Factors Drives the Language Services Market Growth?

Cross-border contact has become more intense due to globalization, increasing the need for translation, localization, and interpretation services. Language solutions are required by growing multinational businesses, e-commerce, and multilingual customer service. Growth is also fueled by government programs that support accessibility and multilingualism. Technology advancements, including AI-driven translation tools, increase productivity and widen the market.

These developments empower businesses to offer better-tailored solutions and services, which, in turn, contribute to the growth of the Language Services industry.

For instance, A well-known international provider of language services, BIG Language Solutions, revealed in April 2022 that it had acquired the Milan-based company Lawlinguists, which offers legal translation services. With the addition of Italy, Germany, and Spain to BIG's European footprint through the purchase, its clients now have access to a wider range of excellent legal translation services, resources, and technology.

(Source:biglanguage.com/blog/big-acquires-lawlinguists-expands-legal-offering-and-european-presence/)

Globalization and Internationalization to Provide Viable Market Output

A significant market driver for language services has been globalization. Communication in various languages is becoming increasingly important as firms grow internationally. The expansion of international trade, e-commerce, and cross-border investments all contribute to this trend. Companies must translate, localize, and adapt their products and services to local languages and cultures to remain competitive in the global market.

There are approximately 7,139 languages spoken in the world today. However, many of these languages are endangered, with experts estimating that around 40% of languages are at risk of extinction.

(Source:www.ohchr.org/en/stories/2019/10/many-indigenous-languages-are-danger-extinction)

Multinational corporations with diverse workforces and clients from various language backgrounds have become popular due to globalization. These enterprises rely on translation services to eliminate language barriers to guarantee efficient internal communication and seamless relations with external parties. Language solutions, including document, website, and marketing material translation and conference and meeting interpretation services, greatly aid international collaboration and understanding.

Technological Advancements to Propel Market Growth





Localization of Digital Content

Factors Restraining Growth of the Language Services Market

Machine Translation Limitations to Hinder Market Growth

The constraints of machine translation constrain the language services market. While machine translation quality has increased due to technological developments in AI, especially for complicated or specialized information, it still falls short of human translation in accuracy and nuance. The context and idiomatic idioms that machine translation systems frequently struggle with might cause translations to sound uncomfortable or inaccurate to native speakers. This restriction is especially important for fields like law, medicine, and marketing, where accuracy and cultural appropriateness are key.

How COVID-19 Impacted the Language Services Market?

To reach a worldwide audience, the pandemic drove digital transformation and remote labor, driving up demand for translation and localization services. Translations in the medical and scientific fields increased as information sharing became essential. Travel restrictions hampered on-site interpreting services simultaneously, increasing the demand for remote interpreting services. Due to the pandemic's emphasis on efficient intercultural communication, businesses, the medical community, and governments have all prioritized language services to enable proper information flow and support during the crisis What is Language Services?

Language services means it is a professional service used for communication and understanding between different cultural groups. It facilitates effective comm...

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.