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.

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.

This statistic presents the leading European countries by their level of English proficiency as of March 2019. According to data provided by Klazz, Sweden had the highest percentage of people who were proficient in English at ** percent of the population.

This dataset, released in August 2017, contains the Australian residents population by their birthplace divided into English speaking (ES) and non-English speaking (NES) countries, 2016. The following countries are designated as ES: Canada, Ireland, New Zealand, South Africa, United Kingdom and the United States of America; the remaining countries are designated as NES. The dataset also includes the population of people born overseas and report poor proficiency in English. The data is by Local Government Area (LGA) 2016 geographic boundaries. For more information please see the data source notes on the data. Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

The United States is the non-hispanic country with the largest number of native Spanish speakers in the world, with approximately 41.89 million people with a native command of the language in 2024. However, the European Union had the largest group of non-native speakers with limited proficiency of Spanish, at around 28 million people. Furthermore, Mexico is the country with the largest number of native Spanish speakers in the world as of 2024.

Using data from reports such as the "English Proficiency Index" (EDU) from Education First, one can see the significant impact of culture, education and globalization on the ability of citizens of different countries to speak English.

People born in the ten most common non-English speaking background countries by SD, for the year 2011.

People born in the ten most common non-English speaking background countries by LGA 2011, for the 2011.

Singapore scored 631 out of a maximum of 800 points in the English Proficiency Index 2022, the highest score across the selected Asian countries and territories. In contrast, Thailand reached an English Proficiency Index score of 416 that year.

People born in the ten most common non-English speaking background countries by SD, for the year 2011.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

Argentina scored 562 out of a maximum of 800 points in the English Proficiency Index 2023. That was the highest score among all Latin American countries included in the survey. The Argentine capital, Buenos Aires, also received the highest English proficiency score among all the Latin American cities analyzed. Mexico and Haiti received the lowest scores in the region.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

Context:

Everyone who speaks a language, speaks it with an accent. A particular accent essentially reflects a person's linguistic background. When people listen to someone speak with a different accent from their own, they notice the difference, and they may even make certain biased social judgments about the speaker.

The speech accent archive is established to uniformly exhibit a large set of speech accents from a variety of language backgrounds. Native and non-native speakers of English all read the same English paragraph and are carefully recorded. The archive is constructed as a teaching tool and as a research tool. It is meant to be used by linguists as well as other people who simply wish to listen to and compare the accents of different English speakers.

This dataset allows you to compare the demographic and linguistic backgrounds of the speakers in order to determine which variables are key predictors of each accent. The speech accent archive demonstrates that accents are systematic rather than merely mistaken speech.

All of the linguistic analyses of the accents are available for public scrutiny. We welcome comments on the accuracy of our transcriptions and analyses.

Content:

This dataset contains 2140 speech samples, each from a different talker reading the same reading passage. Talkers come from 177 countries and have 214 different native languages. Each talker is speaking in English.

This dataset contains the following files:

• reading-passage.txt: the text all speakers read
• speakers_all.csv: demographic information on every speaker
• recording: a zipped folder containing .mp3 files with speech

Acknowledgements:

This dataset was collected by many individuals (full list here) under the supervision of Steven H. Weinberger. The most up-to-date version of the archive is hosted by George Mason University. If you use this dataset in your work, please include the following citation:

Weinberger, S. (2013). Speech accent archive. George Mason University.

This datasets is distributed under a CC BY-NC-SA 2.0 license.

Inspiration:

The following types of people may find this dataset interesting:

• ESL teachers who instruct non-native speakers of English
• Actors who need to learn an accent
• Engineers who train speech recognition machines
• Linguists who do research on foreign accent
• Phoneticians who teach phonetic transcription
• Speech pathologists
• Anyone who finds foreign accent to be interesting

The data collected aim to test whether English proficiency levels in a country are positively associated with higher democratic values in that country. English proficiency is sourced from statistics by Education First’s "EF English Proficiency Index" which covers countries' scores for the calendar year 2022 and 2021. The EF English Proficiency Index ranks 111 countries in five different categories based on their English proficiency scores that were calculated from the test results of 2.1 million adults. While democratic values are operationalized through the liberal democracy index from the V-Dem Institute annual report for 2022 and 2021. Additionally, the data is utilized to test whether English language media consumption acts as a mediating variable between English proficiency and democracy levels in a country, while also looking at other possible regression variables. In order to conduct the linear regression analyses for the dats, the software that was utilized for this research was Microsoft Excel.The raw data set consists of 90 nation states in two years from 2022 and 2021. The raw data is utilized for two separate data sets the first of which is democracy indicators which has the regression variables of EPI, HDI, and GDP. For this table set there is a total of 360 data entries. HDI scores are a statistical summary measure that is developed by the United Nations Development Programme (UNDP) which measures the levels of human development in 190 countries. The data for nominal gross domestic product scores (GDP) are sourced from the World Bank. Having strong regression variables that have been proven to have a positive link with democracy in the data analysis such as GDP and HDI, would allow the regression analysis to identify whether there is a true relationship between English proficiency and democracy levels in a country. While the second data set has a total of 720 data entries and aims to identify English proficiency indicators the data set has 7 various regression variables which include, LDI scores, Years of Mandatory English Education, Heads of States Publicly speaking English, GDP PPP (2021USD), Common Wealth, BBC web traffic and CNN web traffic. The data for years of mandatory English education is sourced from research at the University of Winnipeg and is coded in the data set based on the number of years a country has English as a mandatory subject. The range of this data is from 0 to 13 years of English being mandatory. It is important to note that this data only concerns public schools and does not extend to the private school systems in each country. The data for heads of state publicly speaking English was done through a video data analysis of all heads of state. The data was only used for heads of state who had been in their position for at least a year to ensure the accuracy of the data collected; with a year in power, for heads of state that had not been in their position for a year, data was taken from the previous head of state. This data only takes into account speeches and interviews that were conducted during their incumbency. The data for each country’s GDP PPP scores are sourced from the World Bank, which was last updated for a majority of the countries in 2021 and is tied to the US dollar. Data for the commonwealth will only include members of the commonwealth that have been historically colonized by the United Kingdom. Any country that falls under that category will be coded as 1 and any country that does not will be coded as 0. For BBC and CNN web traffic that data is sourced by using tools in Semrush which provide a rough estimate of how much web traffic each news site generates in each country. Which will be utilized to identify the average number of web traffic for BBC News and CNN World News for both the 2021 and 2022 calendar. The traffic for each country will also be measured per capita, per 10 thousand people to ensure that the population density of a country does not influence the results. The population of each country for both 2021 and 2022 is sourced from the United Nations revision of World Population Prospects of both 2021 and 2022 respectively.

Dataset population: Persons aged 3 and over

Main language (detailed)

The language that is a person's first or preferred language.

This information helps central government, local authorities and the NHS to allocate resources and provide services for non-English speakers, e.g. English teaching and translation services. It is a better indicator than country of birth, which was previously used to forecast the additional cost of providing services to people whose first language is not English.

The data are also used to assess the impact of English or Welsh language ability on employment and other social inclusion indicators.

Information on the number of British Sign Language users helps with service planning and assists in developing policies to address the needs of the deaf community.

These statistics are used by public service providers to effectively target the delivery of their services, for example in the provision of translation and interpretation services, the availability of English language lessons, and the distribution of official information leaflets in alternative languages.

This map shows the predominant language spoken at home by the US population aged 5+. This is shown by Census Tract and County centroids. The data values are from the 2012-2016 American Community Survey 5-year estimates in the S1601 Table for Language Spoken at Home. The popup in the map provides a breakdown of the population age 5+ by the language spoken at home. Data values for other age groups are also available within the data's table. The color of the symbols represent the most common language spoken at home. This predominance map style compares the count of people age 5+ based on what language is spoken at home, and returns the value with the highest count. The census breaks down the population 5+ by the following language options:English OnlyNon-English - SpanishNon-English - Asian and Pacific Islander LanguagesNon-English - Indo European LanguagesNon-English - OtherThe size of the symbols represents how many people are 5 years or older, which helps highlight the quantity of people that live within an area that were sampled for this language categorization. The strength of the color represents HOW predominant an language is within an area. If the symbol is a strong color, it makes up a larger portion of the population. This map is designed for a dark basemap such as the Human Geography Basemap or the Dark Gray Canvas Basemap. See the web map to see the pattern at both the county and tract level. This map helps to show the most common language spoken at home at both a regional and local level. The tract pattern shows how distinct neighborhoods are clustered by which language they speak. The county pattern shows how language is used throughout the country. This pattern is shown by census tracts at large scales, and counties at smaller scales.This data was downloaded from the United States Census Bureau American Fact Finder on January 16, 2018. It was then joined with 2016 vintage centroid points and hosted to ArcGIS Online and into the Living Atlas.Nationally, the breakdown of education for the population 5+ is as follows:Total EstimateMargin of ErrorPercent EstimateMargin of ErrorPopulation 5 years and over298,691,202+/-3,594(X)(X)Speak only English235,519,143+/-154,40978.90%+/-0.1Spanish39,145,066+/-94,57113.10%+/-0.1Asian and Pacific Island languages10,172,370+/-22,5613.40%+/-0.1Other Indo-European languages10,827,536+/-46,3353.60%+/-0.1Other languages3,027,087+/-23,3021.00%+/-0.1

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.

Dataset Description: The AI-Enhanced English and Chinese Language Learning Dataset is a comprehensive collection of data aimed at advancing language education through the use of artificial intelligence. This dataset includes detailed records from various language learning platforms, combining both traditional classroom activities and AI-driven learning applications. The dataset is suitable for exploring different AI techniques to improve English and Chinese language acquisition, focusing on adaptive learning, feedback analysis, and language practice. Data spans from February 2019 to August 2024, covering diverse language learning scenarios across multiple institutions, including digital language labs, mobile apps, and AI-powered tutoring systems.

The dataset includes hourly data collected from language learners engaging in various activities such as grammar exercises, conversational practice, writing assessments, and interactive quizzes. The data is sourced from multiple regions, including English-speaking and Mandarin-speaking communities, making it ideal for comparative studies on AI-driven learning outcomes. The records encompass a variety of linguistic features and learning metrics, offering valuable insights into student engagement, progress, and performance across different learning contexts.

Features: Timestamp: Hourly timestamp indicating the time of each learning session. Learner ID: A unique identifier for each learner. Age: The age of the learner. Gender: Gender of the learner (Male, Female, Other). Native Language: The primary language spoken by the learner. Country of Residence: The country where the learner is based. Language Proficiency Level (Initial): The learner's initial language proficiency in English or Chinese (Beginner, Intermediate, Advanced). Type of Activity: Type of learning activity (Listening, Speaking, Reading, Writing). Lesson Content Type: The specific focus of the lesson (Grammar, Vocabulary, Pronunciation, etc.). Number of Lessons Completed: Cumulative count of lessons completed by the learner. Time Spent on Learning: Total time spent on language learning (in minutes). Learning Platform or Tool Used: Platform or tool used for learning (App, Website, Classroom Software). Homework Completion Rate: Percentage of homework assignments completed. Participation in Interactive Exercises: Frequency of participation in interactive exercises like quizzes and games. Frequency of Practice Sessions: Number of practice sessions per week. Test Scores: Scores from language proficiency tests, covering various areas such as grammar, listening, and vocabulary. Speaking Fluency Scores: Scores evaluating pronunciation accuracy and speech rate. Reading Comprehension Scores: Assessment scores for reading comprehension tasks. Writing Quality: Evaluation of writing quality based on grammatical accuracy and vocabulary use. Change in Proficiency Level: Measured change in language proficiency over time. Assignment Grades: Grades received on language assignments. Error Correction Rate: The rate at which learners correct their mistakes. Feedback from Instructors/Tutors: Qualitative feedback provided by instructors or AI tutors. Study Session Duration: Average duration of study sessions. Learning Consistency: Number of days per week studied. User Activity Type: Type of user activity (Active or Passive Participation). Engagement with Additional Learning Materials: Frequency of accessing extra learning resources (e.g., videos, articles). Peer Interaction Score: Score representing participation in study groups or discussion forums. Motivation Level: Self-reported level of motivation. Learning Environment: Type of learning environment (Home, School, Language Center). Learning Mode: Mode of learning (Self-Paced or Instructor-Led). Accessibility of Learning Resources: Availability of learning materials to the learner. Use of AI Tools: Whether AI tools like chatbots or speech recognition software were used. Language Learning Goals: Purpose of language learning (Academic, Professional, Personal). This dataset offers rich data for researchers and educators to analyze the impact of AI on language learning outcomes, make cross-linguistic comparisons, and develop personalized AI-driven language education models.

English Language Learning Market size was valued at USD 29.48 Billion in 2024 and is projected to reach USD 63.56 Billion by 2032, growing at a CAGR of 10.08% from 2026 to 2032.

Key Market Drivers:

Globalization and International Business Communication: The growing demand for English proficiency in worldwide business is a major driver of the English language learning market. According to a British Council report, 1.75 billion people worldwide speak English at a useful level, with that figure expected to rise to 2 billion by 2020. The expanding use of English in international business situations is driving up the demand for English language learning programs and services.

Growth in International Student Mobility: The growing number of students studying abroad, particularly in English-speaking nations, is driving up demand for English language learning. According to UNESCO, there were over 5.6 million international students worldwide in 2020, with English-speaking countries being popular destinations. This trend increases the need for English language preparatory courses and assessments.