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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2603.28714 |
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| _version_ | 1866913165073186816 |
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| author | Pulikodan, Sujith Singh, Abhayjeet Basu, Agneedh Desai, Nihar J, Pavan Kumar Bhat, Pranav D Dharmaraju, Raghu Gupta, Ritika Udupa, Sathvik Kumar, Saurabh Sharma, Sumit Sanka, Visruth Tewari, Dinesh Dhand, Harsh Kamat, Amrita Singh, Sukhwinder Vashishth, Shikhar Talukdar, Partha Acharya, Raj Ghosh, Prasanta Kumar |
| author_facet | Pulikodan, Sujith Singh, Abhayjeet Basu, Agneedh Desai, Nihar J, Pavan Kumar Bhat, Pranav D Dharmaraju, Raghu Gupta, Ritika Udupa, Sathvik Kumar, Saurabh Sharma, Sumit Sanka, Visruth Tewari, Dinesh Dhand, Harsh Kamat, Amrita Singh, Sukhwinder Vashishth, Shikhar Talukdar, Partha Acharya, Raj Ghosh, Prasanta Kumar |
| contents | Voice based technologies have the potential to bridge digital accessibility gaps; however, existing datasets fail to capture the linguistic and regional diversity of Indic languages. We present Project VAANI, a large scale multimodal dataset designed to represent India's linguistic landscape across 165 districts. Speech data is collected using image based prompts to elicit spontaneous responses, while images are curated through a separate pipeline covering diverse themes across regions. The dataset undergoes a rigorous multi stage quality control process, combining automated and manual evaluation to ensure high audio quality and transcription accuracy. We release approximately 289K images, 31,255 hours of speech, and 2,043 hours of transcribed audio spanning 105 languages from 28 states and 3 union territories. Many of these languages are represented at this scale for the first time, making VAANI a foundational resource for inclusive speech technology. The dataset enables the development of robust, multilingual, and multimodal models, and supports research in speech recognition, language understanding, and cross-modal learning for underrepresented languages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28714 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VAANI: Capturing the language landscape for an inclusive digital India Pulikodan, Sujith Singh, Abhayjeet Basu, Agneedh Desai, Nihar J, Pavan Kumar Bhat, Pranav D Dharmaraju, Raghu Gupta, Ritika Udupa, Sathvik Kumar, Saurabh Sharma, Sumit Sanka, Visruth Tewari, Dinesh Dhand, Harsh Kamat, Amrita Singh, Sukhwinder Vashishth, Shikhar Talukdar, Partha Acharya, Raj Ghosh, Prasanta Kumar Audio and Speech Processing Voice based technologies have the potential to bridge digital accessibility gaps; however, existing datasets fail to capture the linguistic and regional diversity of Indic languages. We present Project VAANI, a large scale multimodal dataset designed to represent India's linguistic landscape across 165 districts. Speech data is collected using image based prompts to elicit spontaneous responses, while images are curated through a separate pipeline covering diverse themes across regions. The dataset undergoes a rigorous multi stage quality control process, combining automated and manual evaluation to ensure high audio quality and transcription accuracy. We release approximately 289K images, 31,255 hours of speech, and 2,043 hours of transcribed audio spanning 105 languages from 28 states and 3 union territories. Many of these languages are represented at this scale for the first time, making VAANI a foundational resource for inclusive speech technology. The dataset enables the development of robust, multilingual, and multimodal models, and supports research in speech recognition, language understanding, and cross-modal learning for underrepresented languages. |
| title | VAANI: Capturing the language landscape for an inclusive digital India |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2603.28714 |