This statistic represents results of a survey about the share of English speakers across India in 2019, by region. During the surveyed time period, the share of respondents who spoke English in urban areas was around ** percent while this was about ***** percent for rural respondents.

The statistic displays the number of native English speakers in India from 1971 to 2011. About *** thousand Indians recognized English as their mother-tongue according to the 2011 census, up from about ***** thousand speakers in the census of 2001.

Nearly 260,000 speakers reported to speak English as their mother-tongue in India as per the latest census. Of these, Maharastra had the highest number of English speakers, followed by Tamil Nadu.

This statistic displays the number of Indian and English language internet users across India from 2011 to 2021. In 2016, the number of English internet users amounted to about *** million and was projected to increase to *** million in 2021. For Indian language users, this number was about *** million users in 2016, and was projected to reach *** million in 2021.

Introduction

Welcome to the Indian English Scripted Monologue Speech Dataset for the Retail & E-commerce Domain. This meticulously curated dataset is designed to advance the development of English language speech recognition models, particularly for the Retail & E-commerce industry.

Speech Data

This training dataset comprises over 6,000 high-quality scripted prompt recordings in Indian English. These recordings cover various topics and scenarios relevant to the Retail & E-commerce domain, designed to build robust and accurate customer service speech technology.

Each scripted prompt is crafted to reflect real-life scenarios encountered in the Retail & E-commerce domain, ensuring applicability in training robust natural language processing and speech recognition models.

Transcription Data

In addition to high-quality audio recordings, the dataset includes meticulously prepared text files with verbatim transcriptions of each audio file. These transcriptions are essential for training accurate and robust speech recognition models.

This dataset provides information on 13,127 in India as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Introduction

Welcome to the Indian English General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of English speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Indian English communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade English speech models that understand and respond to authentic Indian accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Indian English. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple English speech and language AI applications:

English(India) Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering generic domain, human-machine interaction, smart home command and in-car command, numbers and other domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers( 2,100 Indian native speakers), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Svarah: An Indic Accented English Speech Dataset

  Overview

India is the second largest English-speaking country in the world, with a speaker base of roughly 130 million. Unfortunately, Indian speakers are underrepresented in many existing English ASR benchmarks such as LibriSpeech, Switchboard, and the Speech Accent Archive. To address this gap, we introduce Svarah—a benchmark that comprises 9.6 hours of transcribed English audio from 117 speakers across 65… See the full description on the dataset page: https://huggingface.co/datasets/ai4bharat/Svarah.

This statistic represents the forecast for share of non-English internet users across India in 2020, based on language. Hindi was projected to have the highest share of internet users in the country with about ** percent, while the share was about ***** percent for Malayalam during the measured time period.

This dataset provides information on 86 in Uttar Pradesh, India as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

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.

Residents of Australia who were born overseas in one of the predominantly non-English speaking countries which are in the top ten for Australia in terms of high numbers of migrants, 2011 (highest to lowest: China, India, Italy, Vietnam, Philippines, Malaysia, Germany, Greece, Sri Lanka and Lebanon) (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). Source: Compiled by PHIDU based on ABS Census 2011 data.

The audio dataset includes Call Center Conversation, featuring Indian English speakers from India with detailed metadata.

Introduction

This Indian English Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for English-speaking telecom customers. Featuring over 30 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Indian English speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

Metadata

Rich metadata is available for each participant and conversation:

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.

This dataset provides information on 63 in Tamil Nadu, India as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information on 61 in West Bengal, India as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Home Indian English DatasetHigh-Quality Indian English Wake Word Dataset for AI & Speech Models Contact Us OverviewTitleIndian English Language DatasetDataset TypeWake WordDescriptionWake Words / Voice Command / Trigger Word /…

English Proficiency Test Market Outlook

The global market size for English proficiency tests was valued at approximately USD 2.8 billion in 2023 and is projected to reach around USD 5.1 billion by 2032, registering a compound annual growth rate (CAGR) of 6.5% during the forecast period. The growth of the English Proficiency Test market is primarily driven by the increasing globalization of educational and professional opportunities, coupled with the rising importance of English as a global lingua franca.

One of the significant growth factors for the English Proficiency Test market is the burgeoning demand for higher education in English-speaking countries such as the United States, the United Kingdom, Canada, and Australia. Students from non-English speaking countries are increasingly required to demonstrate their English language capabilities to gain admission into these institutions. This demand has led to the proliferation of various standardized English tests tailored to assess the language proficiency of non-native speakers. Additionally, the increasing number of international student exchange programs and scholarships further propels the demand for these tests.

Another key driver is the growing trend of global migration for employment purposes. Many multinational corporations and organizations require proof of English proficiency during the hiring process, especially for roles that necessitate extensive communication with international clients or teams. Governments in English-speaking nations have also established English language proficiency as a prerequisite for work visas and immigration, further bolstering the market. The globalization of the workforce and the rise of remote working models have added to the demand for standardized English tests.

Technological advancements in education and assessment systems have also significantly contributed to market growth. The advent of online testing platforms has made it easier for candidates to take English proficiency tests from any location, thereby increasing accessibility and convenience. Online platforms also enable advanced features like instant scoring, personalized feedback, and adaptive testing, making the assessment process more efficient and user-friendly. These technological innovations are expected to continue driving market expansion.

Regionally, the Asia-Pacific region exhibits the highest growth potential in the English Proficiency Test market. Countries like China, India, and South Korea are investing heavily in English education to enhance global competitiveness. The region's growing middle class and increasing emphasis on education and professional development contribute to the rising demand for English proficiency tests. Additionally, regional policies encouraging international education and employment opportunities further support market growth in this region.

Test Type Analysis

The English Proficiency Test market is segmented by test type, including IELTS, TOEFL, PTE, Cambridge English Exams, and others. IELTS (International English Language Testing System) holds a significant share due to its widespread acceptance by educational institutions, employers, and immigration authorities in English-speaking countries. The comprehensive nature of the IELTS test, which evaluates listening, reading, writing, and speaking skills, makes it a preferred choice for many candidates. Continuous updates to the test format and scoring mechanisms also keep it relevant and widely recognized.

The TOEFL (Test of English as a Foreign Language) is another dominant segment, particularly favored by academic institutions in the United States and Canada. TOEFL's focus on academic English makes it suitable for students aiming to pursue higher education in these countries. The test's integration with digital platforms for registration, preparation, and results distribution enhances its accessibility and appeal. The availability of various TOEFL test versions, including the internet-based test (iBT) and the paper-delivered test, caters to different candidate preferences and regional constraints.

The PTE (Pearson Test of English) Academic has been gaining traction due to its fully computerized format and quick result turnaround. Its algorithmic scoring system reduces human bias and provides a more objective assessment of English proficiency. The PTE Academic test is recognized by numerous universities and governments, particularly in Australia and New Zealand, making it a popular choice among students and immigrants. Continuous improvements in test delivery and scorin