RESPIN-S1.0 Corpus: A read speech corpus of 10000+ hours in dialects of nine Indian Languages
Saurabh Kumar, Abhayjeet Singh, DEEKSHITHA G, Amartya veer, Jesuraj Bandekar, Savitha Murthy, Sumit Sharma, Sandhya Badiger, sathvik udupa, Amala Nagireddi, Srinivasa Raghavan K M, Rohan Saxena, Jai Nanavati, Raoul Nanavati, Janani Sridharan, Arjun Mehta, Ashish S, Sai Mora, Prashanthi Venkataramakrishnan, Gauri Date, Karthika P, Prasanta Ghosh
We introduce RESPIN-S1.0, the largest publicly available dialect-rich read speech corpus for Indian languages, comprising over 10,000 hours of validated audio spanning nine major languages -- Bengali, Bhojpuri, Chhattisgarhi, Hindi, Kannada, Magahi, Maithili, Marathi, and Telugu. Indian languages are characterized by high dialectal variation and are spoken by populations that are often digitally underserved. Existing speech corpora typically represent only standard dialects and lack domain relevance. RESPIN-S1.0 fills this critical gap by collecting speech across 38+ dialects and two high-impact domains: agriculture and finance. Text data was carefully composed by native dialect speakers and validated via a robust pipeline involving both automatic and manual checks. Over 200,000 utterances were recorded through a crowdsourced mobile application by native speakers and subsequently categorized into clean, semi-noisy, and noisy slabs based on transcription quality. The clean slab alone exceeds 10,000 hours. RESPIN also provides speaker metadata, phonetic lexicons, and dialect-aware train-dev-test splits to ensure reproducibility. To benchmark performance, we evaluate a range of ASR models -- TDNN-HMM, E-Branchformer, Whisper, IndicWav2Vec2, and SPRING SSL models -- and find that fine-tuning on RESPIN significantly improves recognition accuracy over existing pretrained models. A subset of RESPIN-S1.0 has already supported community efforts through challenges such as the SLT Code Hackathon 2022 and MADASR@ASRU 2023/2025, with over 1200 hours of data released publicly. This resource supports research in dialectal ASR, LID, DID, and speech-related areas, and sets a new standard for inclusive, dialect-rich corpora in multilingual, low-resource settings.