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.

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 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.

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 French corpus was produced using Le Monde newspaper. It contains recordings of 100 speakers (49 males, 51 females) recorded in Grenoble, France. The following age distribution has been obtained: 3 speakers are below 19, 52 speakers are between 20 and 29, 16 speakers are between 30 and 39, 13 speakers are between 40 and 49, and 14 speakers are over 50 (2 speakers 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.

Language is a controlled vocabulary that lists world languages and language varieties, including sign languages. Its main purpose is to support activities associated with the publication process. The full set of languages contains more than 8000 language varieties, each identified by a code equivalent to the ISO 639-3 code. Concepts are aligned with the ISO 639 international standard, which is issued in several parts: ISO 639-1 contains strictly two alphabetic letters (alpha-2), ISO 639-2/B (B = bibliographic) is used for bibliographic purpose (alpha-3), ISO 639-2/T (T = terminology) is used for technical purpose (alpha-3), ISO 639-3 covers all the languages and macro-languages of the world (alpha-3); the values are compliant with ISO 639-2/T. If an authority code is needed for a language without an assigned ISO code, an alphanumeric code is created to avoid confusion with the strictly alphabetic ISO codes. Labels are provided in all 24 official EU languages for the most frequently used languages. Language is under governance of the Interinstitutional Metadata and Formats Committee (IMFC). It is maintained by the Publications Office of the European Union and disseminated on the EU Vocabularies website. It is a corporate reference data asset covered by the Corporate Reference Data Management policy of the European Commission.

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 Korean corpus was produced using the Hankyoreh Daily News. It contains recordings of 100 speakers (50 males, 50 females) recorded in Seoul, Korea. The following age distribution has been obtained: 7 speakers are below 19, 70 speakers are between 20 and 29, 19 speakers are between 30 and 39, and 3 speakers are between 40 and 49 (1 speaker age is unknown).

While English is the official language, it is typically used for governmental, business, and media purposes. In day to day life most people in the country speak Krio, which is a style of Pidgin English or English-based creole language. Krio is the lingua franco for the country and the formal language for those who do not speak English. With the number of different ethnic groups, Krio unites these groups with a common language. The citizens who are fluent in English are among the elite minority and often experience privileges such as economic opportunities that non-English speakers are excluded from. Other common indigenous languages used in the country are Mende, Temne, and Limba. As the official language, English is the only language used in education. It is reported that school children who speak indigenous languages on school premises are punished. Students who fail English classes are not granted admission into college. Attribute Table Field DescriptionsISO3-International Organization for Standardization 3-digit country codeADM0_NAME-Administration level zero identification / nameLANG_FAM-Language familyLANG_SUBGR-Language subgroupALT_NAMES-Alternate namesCOMMENTS-Comments or notes regarding languageSOURCE_DT-Source one creation dateSOURCE-Source oneSOURCE2_DT-Source two creation dateSOURCE2-Source twoCollectionThis feature class was created using Anthromapper consisting of linguistic layers that have been primarily based on The World Language Mapping System (WMLS). Geographical terrain features, combined with a watershed model, were also used to predict the likely extent of linguistic influence. The metadata was supplemented with anthropological and linguistic information from peer-reviewed journals and published books. It should be noted that this feature class only depicts the majority first level languages spoken in a given area; there might be significant populations of other minority language speakers not shown in this dataset.The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)Anthromapper. DigitalGlobe, November 2014.Ethnologue, “Languages of the World." 2012. Accessed November 2014. http://www.ethnologue.com.World Language Mapping System (WLMS) Version 16. World GeoDatasets, November 2014.Sources (Metadata)Antimoon, “English, French, and Arabic languages in Sierra Leone”. December 2009. Accessed December 2014. http://www.antimoon.com.Central Intelligence Agency. The World FactBook, “Serra Leone”. June 2014. Accessed November 2014. https://www.cia.gov/library/publications/the-world-factbook.DePauw University. Sierra Leone, “Language”. January 2014. Accessed December 2014. http://www.depauw.edu.National African Language Resource Center (NALRC), “Krio”. January 2014. Accessed December 2014. http://www.nalrc.indiana.edu.

BackgroundHearing loss is known to be a serious issue that impedes human communication. The World Health Organization (WHO) estimates that approximately 20 in 100,000 newborns demonstrate congenital hearing impairments, leading to severely impacted language, academic, and social abilities of these children.ObjectiveThe reduced quality of life and work productivity among hearing-impaired individuals eventually affects societal outcomes and development. Since limited studies address the nature of hearing-impaired individuals in Jordan, this research aimed to define the prevalence and nature of hearing loss in Jordan, highlighting important facts about hearing loss epidemiology across Jordanians.MethodsThe current research focused on assessing hearing function for 1000 individuals over 12 years to define the rate, most prominent configurations, and the most common characteristics of hearing difficulties in Jordan.ResultsThe results showed that sixty-three per 1,000 people have hearing loss, most frequently sensorineural hearing loss. The age range of people with hearing loss was 12 to 89 years old, with a median age of 51. The incidence of hearing loss appeared at a later age (33.33%, X2 = 15.74, p0.05), with sensorineural hearing loss reported to be the most common type of hearing loss (N = 46, 73.00%), and mild is the most frequent severity (N = 25, X2 = 23.58, p

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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.

For a reader convenience the lists of all 100 ranked names for all 24 Wikipedia editions and corresponding network link data for each edition are also given at [39] in addition to Supporting Information file. All used computational data are publicly available at http://dumps.wikimedia.org/. All the raw data necessary to replicate the findings and conclusion of this study are within the paper, supporting information files and this Wikimedia web site. (PDF)

This dataset displays information regarding the language spoken most often at home. This data is available on the Census Division level, and is available from the 2006 Canadian Census. This data was obtained through: Statistics Canada. This data refers to the language spoken most often at home by the individual at the time of the census. Other languages spoken at home on a regular basis were also collected. Included are population figures for the following attributes: Total Population, English, French, Non-Official, English and French, English and Non-Official Language, French and Non-Official Language, and English French and Non-Official Speaking. This data is also broken down by Age Group.

(UNCLASSIFIED) English is the official language in Liberia and is used in government, business, and education to some extent. The majority of Liberians do not know English and those who do speak what is commonly referred to as Liberian English. This involves often leaving off the end of words and/or adding the letter “o” to the end. Also English words will have different meaning in the country. Prior to the civil wars the government created the National Language Program. This program was designed to introduce local languages to primary students prior to the instruction of English. Due to two civil wars and poor infrastructure langue policy has not received much attention. While English is the official language it does not properly represent the diverse population leaving few to be fluent and causing them to rely on their local languages. Attribute Table Field DescriptionsISO3 - International Organization for Standardization 3-digit country code ADM0_NAME - Administration level zero identification / name LANG_FAM - Language family LANG_SUBGR - Language subgroup ALT_NAMES - Alternate names COMMENTS - Comments or notes regarding language SOURCE_DT - Source one creation date SOURCE - Source one SOURCE2_DT - Source two creation date SOURCE2 - Source two CollectionThis HGIS was created through linguistic information provided through The World Language Mapping System (WMLS). This data was then processed through DigitalGlobe’s AnthroMapper program to generate more accurate linguistic coverage boundaries. The metadata was supplemented with anthropological and linguistic information from peer-reviewed journals and published books. It should be noted that this shape file only depicts the majority first level languages spoken in a given area; there might be significant populations of other minority language speakers not shown in this dataset. The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)Anthromapper. DigitalGlobe, September 2014.World Language Mapping System (WLMS) Version 16. World GeoDatasets, October 2013.Sources (Metadata)Albaugh, Ericka. "Language Policies in African Education." working paper., Department of Government Legal Studies at Bowdoin College, 2005. http://www.bowdoin.edu/.Central Intelligence Agency. The World FactBook, “Liberia”. Last updated June 2014. Accessed September 2014. https://www.cia.gov/index.html.The Reeds in Liberia, “Liberian English.” October 2007. Accessed September 2014. http://reedsinliberia.blogspot.com/.

Bilingual Education for Children Market Outlook

The global market size for Bilingual Education for Children is projected to grow significantly from $5.2 billion in 2023 to approximately $12.5 billion by 2032, boasting a Compound Annual Growth Rate (CAGR) of 10%. Several factors contribute to this robust growth, including increasing globalization, a heightened emphasis on multicultural competencies, and the growing recognition of the cognitive benefits associated with bilingualism.

One of the primary growth drivers for the bilingual education market is the increasing globalization and interconnectivity of economies. As businesses expand across borders and as migration rates rise, the demand for multilingual capabilities among the younger population has surged. Parents and educators alike recognize the importance of equipping children with the skills necessary to thrive in a globalized world, where being fluent in multiple languages can provide a competitive edge in the job market and open up a plethora of opportunities.

Moreover, an increasing body of research highlighting the cognitive benefits of bilingualism is fostering greater acceptance and enthusiasm for bilingual education. Studies have shown that bilingual children tend to have better problem-solving skills, improved memory, and enhanced cognitive flexibility compared to their monolingual peers. These cognitive advantages, coupled with the cultural enrichment that comes from being proficient in more than one language, are driving parents to seek bilingual education programs for their children.

Government policies and educational reforms in various countries are also contributing significantly to the growth of the bilingual education market. Many nations are recognizing the importance of bilingualism and are incorporating language learning into their educational curriculums. For instance, the European Union has a long-standing policy of multilingualism, encouraging citizens to learn at least two foreign languages. Similarly, countries like Canada and the United States have various state and federal programs that support bilingual education in public schools.

Regionally, North America and Europe are leading the market, attributing to their diverse populations and strong emphasis on multicultural education. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This surge can be attributed to the rising middle-class population, increased investments in education, and a growing emphasis on English proficiency, which is seen as a gateway to global opportunities.

In the context of bilingual education, English Picture Books for Children play a crucial role in facilitating language acquisition and literacy development. These books serve as an engaging tool for young learners, combining visual storytelling with simple text to enhance comprehension and retention. By integrating picture books into bilingual programs, educators can create a more immersive and enjoyable learning experience for children. The use of picture books not only aids in vocabulary building but also introduces cultural narratives, helping children connect with diverse perspectives. As the demand for bilingual education grows, the incorporation of English picture books becomes increasingly significant, offering a bridge between languages and fostering a love for reading among children.

Program Type Analysis

The Program Type segment in the bilingual education for children market is divided into various subcategories, including Dual Language Immersion, Transitional Bilingual Education, Two-Way Bilingual Education, and Others. Dual Language Immersion programs have been particularly popular due to their balanced approach, where students are taught in two languages for an equal amount of time. This method not only fosters language proficiency but also ensures that content learning is not compromised. Schools and institutions are increasingly adopting this approach, which is reflected in the growing investments and enrollments in Dual Language Immersion programs.

Transitional Bilingual Education is another significant sub-segment that aims to provide students with the necessary skills to transition from their native language to the target language, usually the languag

Demographic characteristics of 63 individuals with hearing loss.

The statistic reflects the distribution of languages in Canada in 2022. In 2022, 87.1 percent of the total population in Canada spoke English as their native tongue.

Knowledge is central to human and scientific developments. Natural Language Processing (NLP) allows automated analysis and creation of knowledge. Data is a crucial NLP and machine learning ingredient. The scarcity of open datasets is a well-known problem in the machine and deep learning research. This is very much the case for textual NLP datasets in English and other major world languages. For the Bangla language, the situation is even more challenging and the number of large datasets for NLP research is practically nil. We hereby present Potrika, a large single-label Bangla news article textual dataset curated for NLP research from six popular online news portals in Bangladesh (Jugantor, Jaijaidin, Ittefaq, Kaler Kontho, Inqilab, and Somoyer Alo) for the period 2014-2020. The articles are classified into eight distinct categories (National, Sports, International, Entertainment, Economy, Education, Politics, and Science & Technology) providing five attributes (News Article, Category, Headline, Publication Date, and Newspaper Source). The raw dataset contains 185.51 million words and 12.57 million sentences contained in 664,880 news articles. Moreover, using NLP augmentation techniques, we create from the raw (unbalanced) dataset another (balanced) dataset comprising 320,000 news articles with 40,000 articles in each of the eight news categories. Potrika contains both datasets (raw and balanced) to suit a wide range of NLP research. By far, to the best of our knowledge, Potrika is the largest and the most extensive dataset for news classification.

Further details of the dataset, its collection, and usage for deep journalism including detection of the multi-perspective parameters for transportation can be found in our article here: https://doi.org/10.3390/su14095711.

All names are represented by article titles in the English Wikipedia. Here, ΘA is the ranking score of the algorithm A (Eq.3); NA is the number of appearances of a given person in the top 100 rank for all editions. Here CC is the birth country code and LC is the language code of the given historical figure.