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Indian English General Conversation Speech Dataset for ASR
- futurebeeai.com
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EmailClick to copy linkLink copiedCiteFutureBee AI (2022). Indian English General Conversation Speech Dataset for ASR [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/general-conversation-english-indiawavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Dataset funded byFutureBeeAIDescriptionIntroduction
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.
•Participant Diversity:•Speakers: 60 verified native Indian English speakers from FutureBeeAI’s contributor community.•Regions: Representing various provinces of India to ensure dialectal diversity and demographic balance.•Demographics: A balanced gender ratio (60% male, 40% female) with participant ages ranging from 18 to 70 years.•Recording Details:•Conversation Style: Unscripted, spontaneous peer-to-peer dialogues.•Duration: Each conversation ranges from 15 to 60 minutes.•Audio Format: Stereo WAV files, 16-bit depth, recorded at 16kHz sample rate.•Environment: Quiet, echo-free settings with no background noise.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.
•Sample Topics Include:•Family & Relationships•Food & Recipes•Education & Career•Healthcare Discussions•Social Issues•Technology & Gadgets•Travel & Local Culture•Shopping & Marketplace Experiences, and many more.Transcription
Each audio file is paired with a human-verified, verbatim transcription available in JSON format.
•Transcription Highlights:•Speaker-segmented dialogues•Time-coded utterances•Non-speech elements (pauses, laughter, etc.)•High transcription accuracy, achieved through double QA pass, average WER < 5%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:
•Speaker Metadata: Age, gender, accent, dialect, state/province, and participant ID.•Recording Metadata: Topic, duration, audio format, device type, and sample rate.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:
•ASR Development: Train accurate speech-to-text systems for Indian English.•Voice Assistants: Build smart assistants capable of understanding natural Indian conversations.<div style="margin-top:10px; margin-bottom: 10px; padding-left: 30px; display: flex; gap: 16px; align-items: The ORBIT (Object Recognition for Blind Image Training)-India Dataset
- zenodo.org
- data.niaid.nih.gov
- +1more
Updated Apr 24, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCiteGesu India; Gesu India; Martin Grayson; Martin Grayson; Daniela Massiceti; Daniela Massiceti; Cecily Morrison; Cecily Morrison; Simon Robinson; Simon Robinson; Jennifer Pearson; Jennifer Pearson; Matt Jones; Matt Jones (2025). The ORBIT (Object Recognition for Blind Image Training)-India Dataset [Dataset]. http://doi.org/10.5281/zenodo.12608444Unique identifierhttps://doi.org/10.5281/zenodo.12608444Dataset updatedApr 24, 2025AuthorsGesu India; Gesu India; Martin Grayson; Martin Grayson; Daniela Massiceti; Daniela Massiceti; Cecily Morrison; Cecily Morrison; Simon Robinson; Simon Robinson; Jennifer Pearson; Jennifer Pearson; Matt Jones; Matt JonesLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyArea coveredIndiaDescriptionThe ORBIT (Object Recognition for Blind Image Training) -India Dataset is a collection of 105,243 images of 76 commonly used objects, collected by 12 individuals in India who are blind or have low vision. This dataset is an "Indian subset" of the original ORBIT dataset [1, 2], which was collected in the UK and Canada. In contrast to the ORBIT dataset, which was created in a Global North, Western, and English-speaking context, the ORBIT-India dataset features images taken in a low-resource, non-English-speaking, Global South context, a home to 90% of the world’s population of people with blindness. Since it is easier for blind or low-vision individuals to gather high-quality data by recording videos, this dataset, like the ORBIT dataset, contains images (each sized 224x224) derived from 587 videos. These videos were taken by our data collectors from various parts of India using the Find My Things [3] Android app. Each data collector was asked to record eight videos of at least 10 objects of their choice.
Collected between July and November 2023, this dataset represents a set of objects commonly used by people who are blind or have low vision in India, including earphones, talking watches, toothbrushes, and typical Indian household items like a belan (rolling pin), and a steel glass. These videos were taken in various settings of the data collectors' homes and workspaces using the Find My Things Android app.
The image dataset is stored in the ‘Dataset’ folder, organized by folders assigned to each data collector (P1, P2, ...P12) who collected them. Each collector's folder includes sub-folders named with the object labels as provided by our data collectors. Within each object folder, there are two subfolders: ‘clean’ for images taken on clean surfaces and ‘clutter’ for images taken in cluttered environments where the objects are typically found. The annotations are saved inside a ‘Annotations’ folder containing a JSON file per video (e.g., P1--coffee mug--clean--231220_084852_coffee mug_224.json) that contains keys corresponding to all frames/images in that video (e.g., "P1--coffee mug--clean--231220_084852_coffee mug_224--000001.jpeg": {"object_not_present_issue": false, "pii_present_issue": false}, "P1--coffee mug--clean--231220_084852_coffee mug_224--000002.jpeg": {"object_not_present_issue": false, "pii_present_issue": false}, ...). The ‘object_not_present_issue’ key is True if the object is not present in the image, and the ‘pii_present_issue’ key is True, if there is a personally identifiable information (PII) present in the image. Note, all PII present in the images has been blurred to protect the identity and privacy of our data collectors. This dataset version was created by cropping images originally sized at 1080 × 1920; therefore, an unscaled version of the dataset will follow soon.
This project was funded by the Engineering and Physical Sciences Research Council (EPSRC) Industrial ICASE Award with Microsoft Research UK Ltd. as the Industrial Project Partner. We would like to acknowledge and express our gratitude to our data collectors for their efforts and time invested in carefully collecting videos to build this dataset for their community. The dataset is designed for developing few-shot learning algorithms, aiming to support researchers and developers in advancing object-recognition systems. We are excited to share this dataset and would love to hear from you if and how you use this dataset. Please feel free to reach out if you have any questions, comments or suggestions.
REFERENCES:
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Daniela Massiceti, Lida Theodorou, Luisa Zintgraf, Matthew Tobias Harris, Simone Stumpf, Cecily Morrison, Edward Cutrell, and Katja Hofmann. 2021. ORBIT: A real-world few-shot dataset for teachable object recognition collected from people who are blind or low vision. DOI: https://doi.org/10.25383/city.14294597
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microsoft/ORBIT-Dataset. https://github.com/microsoft/ORBIT-Dataset
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Linda Yilin Wen, Cecily Morrison, Martin Grayson, Rita Faia Marques, Daniela Massiceti, Camilla Longden, and Edward Cutrell. 2024. Find My Things: Personalized Accessibility through Teachable AI for People who are Blind or Low Vision. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems (CHI EA '24). Association for Computing Machinery, New York, NY, USA, Article 403, 1–6. https://doi.org/10.1145/3613905.3648641
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Audio Visual Speech Dataset: Indian English
- futurebeeai.com
wavUpdated Aug 1, 2022+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteFutureBee AI (2022). Audio Visual Speech Dataset: Indian English [Dataset]. https://www.futurebeeai.com/dataset/multi-modal-dataset/indian-english-visual-speech-datasetwavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Dataset funded byFutureBeeAIDescriptionIntroduction
Welcome to the Indian English Language Visual Speech Dataset! This dataset is a collection of diverse, single-person unscripted spoken videos supporting research in visual speech recognition, emotion detection, and multimodal communication.
Dataset Content
This visual speech dataset contains 1000 videos in Indian English language each paired with a corresponding high-fidelity audio track. Each participant is answering a specific question in a video in an unscripted and spontaneous nature.
•Participant Diversity:•Speakers: The dataset includes visual speech data from more than 200 participants from different states/provinces of India.•Regions: Ensures a balanced representation of Skip 3 accents, dialects, and demographics.•Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.Video Data
While recording each video extensive guidelines are kept in mind to maintain the quality and diversity.
•Recording Details:•File Duration: Average duration of 30 seconds to 3 minutes per video.•Formats: Videos are available in MP4 or MOV format.•Resolution: Videos are recorded in ultra-high-definition resolution with 30 fps or above.•Device: Both the latest Android and iOS devices are used in this collection.•Recording Conditions: Videos were recorded under various conditions to ensure diversity and reduce bias:•Indoor and Outdoor Settings: Includes both indoor and outdoor recordings.•Lighting Variations: Captures videos in daytime, nighttime, and varying lighting conditions.•Camera Positions: Includes handheld and fixed camera positions, as well as portrait and landscape orientations.•Face Orientation: Contains straight face and tilted face angles.•Participant Positions: Records participants in both standing and seated positions.•Motion Variations: Features both stationary and moving videos, where participants pass through different lighting conditions.•Occlusions: Includes videos where the participant's face is partially occluded by hand movements, microphones, hair, glasses, and facial hair.•Focus: In each video, the participant's face remains in focus throughout the video duration, ensuring the face stays within the video frame.•Video Content: In each video, the participant answers a specific question in an unscripted manner. These questions are designed to capture various emotions of participants. The dataset contain videos expressing following human emotions:•Happy•Sad•Excited•Angry•Annoyed•Normal•Question Diversity: For each human emotion participant answered a specific question expressing that particular emotion.Metadata
The dataset provides comprehensive metadata for each video recording and participant:
• Nexdata | Hindi and English Bilingual Spontaneous Monologue smartphone...
- datarade.ai
Updated Nov 12, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCiteNexdata (2025). Nexdata | Hindi and English Bilingual Spontaneous Monologue smartphone speech dataset | 302 Person [Dataset]. https://datarade.ai/data-products/nexdata-hindi-and-english-bilingual-spontaneous-monologue-s-nexdata.bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formatsDataset updatedNov 12, 2025Dataset authored and provided byNexdataArea coveredIndiaDescriptionHindi and English Bilingual Spontaneous Monologue smartphone speech dataset, collected from dialogues based on given topics, covering generic domain. Our dataset was collected from extensive and diversify speakers(302 people in total, ages 18 to 46), 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.
Format
16k Hz, 16 bit, wav, mono channel
Content category
Individuals naturally speaking, with no specific content limitations. Each speaker records 20 audios in each language (40 recordings per person), each recording lasting about 10-20 seconds
Recording condition
Quiet indoor environment, without echoes, background voices, obvious noises
Recording device
Android phone, iPhone
Speaker
Total 302 contributors,45% male and 55% female. 291contributors aged 18-37, 10 contributors aged 38-45, and 1 contributor aged 46-65
Country
India(IND)
Language
Hindi,English
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Indian English Call Center Data for Travel AI
- futurebeeai.com
wavUpdated Aug 1, 2022+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteFutureBee AI (2022). Indian English Call Center Data for Travel AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/travel-call-center-conversation-english-indiawavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Area coveredIndiaDataset funded byFutureBeeAIDescriptionIntroduction
This Indian English Call Center Speech Dataset for the Travel industry is purpose-built to power the next generation of voice AI applications for travel booking, customer support, and itinerary assistance. With over 30 hours of unscripted, real-world conversations, the dataset enables the development of highly accurate speech recognition and natural language understanding models tailored for English -speaking travelers.
Created by FutureBeeAI, this dataset supports researchers, data scientists, and conversational AI teams in building voice technologies for airlines, travel portals, and hospitality platforms.
Speech Data
The dataset includes 30 hours of dual-channel audio recordings between native Indian English speakers engaged in real travel-related customer service conversations. These audio files reflect a wide variety of topics, accents, and scenarios found across the travel and tourism industry.
•Participant Diversity:•Speakers: 60 native Indian English contributors from our verified pool.•Regions: Covering multiple India provinces to capture accent and dialectal variation.•Participant Profile: Balanced representation of age (18–70) and gender (60% male, 40% female).•Recording Details:•Conversation Nature: Naturally flowing, spontaneous customer-agent calls.•Call Duration: Between 5 and 15 minutes per session.•Audio Format: Stereo WAV, 16-bit depth, at 8kHz and 16kHz.•Recording Environment: Captured in controlled, noise-free, echo-free settings.Topic Diversity
Inbound and outbound conversations span a wide range of real-world travel support situations with varied outcomes (positive, neutral, negative).
•Inbound Calls:•Booking Assistance•Destination Information•Flight Delays or Cancellations•Support for Disabled Passengers•Health and Safety Travel Inquiries•Lost or Delayed Luggage, and more•Outbound Calls:•Promotional Travel Offers•Customer Feedback Surveys•Booking Confirmations•Flight Rescheduling Alerts•Visa Expiry Notifications, and othersThese scenarios help models understand and respond to diverse traveler needs in real-time.
Transcription
Each call is accompanied by manually curated, high-accuracy transcriptions in JSON format.
•Transcription Includes:•Speaker-Segmented Dialogues•Time-Stamped Segments•Non-speech Markers (e.g., pauses, coughs)•High transcription accuracy by dual-layered transcription review ensures word error rate under 5%.Metadata
Extensive metadata enriches each call and speaker for better filtering and AI training:
•Participant Metadata: ID, age, gender, region, accent, and dialect.•Conversation Metadata: Topic, domain, call type, sentiment, and audio specs.Usage and Applications
This dataset is ideal for a variety of AI use cases in the travel and tourism space:
•ASR Systems: Train English speech-to-text engines for travel platforms.<div style="margin-top:10px; margin-bottom: 10px; padding-left: - F
Indian English Call Center Data for Telecom AI
- futurebeeai.com
wavUpdated Aug 1, 2022+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteFutureBee AI (2022). Indian English Call Center Data for Telecom AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/telecom-call-center-conversation-english-indiawavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Dataset funded byFutureBeeAIDescriptionIntroduction
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.
•Participant Diversity:•Speakers: 60 native Indian English speakers from our verified contributor pool.•Regions: Representing multiple provinces across India to ensure coverage of various accents and dialects.•Participant Profile: Balanced gender mix (60% male, 40% female) with age distribution from 18 to 70 years.•Recording Details:•Conversation Nature: Naturally flowing, unscripted interactions between agents and customers.•Call Duration: Ranges from 5 to 15 minutes.•Audio Format: Stereo WAV files, 16-bit depth, at 8kHz and 16kHz sample rates.•Recording Environment: Captured in clean conditions with no echo or background noise.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.
•Inbound Calls:•Phone Number Porting•Network Connectivity Issues•Billing and Payments•Technical Support•Service Activation•International Roaming Enquiry•Refund Requests and Billing Adjustments•Emergency Service Access, and others•Outbound Calls:•Welcome Calls & Onboarding•Payment Reminders•Customer Satisfaction Surveys•Technical Updates•Service Usage Reviews•Network Complaint Status Calls, and moreThis 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.
•Transcription Includes:•Speaker-Segmented Dialogues•Time-coded Segments•Non-speech Tags (e.g., pauses, coughs)•High transcription accuracy with word error rate < 5% thanks to dual-layered quality checks.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:
•Participant Metadata: ID, age, gender, accent, dialect, and location.<div style="margin-top:10px; margin-bottom: 10px; padding-left: 30px; display: flex; gap: 16px; 
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Indian English General Conversation Speech Corpus
https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
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: