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IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages

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arxiv 2403.01926 v1 pith:B4O7PED4 submitted 2024-03-04 cs.CL

Tahir Javed , Janki Atul Nawale , Eldho Ittan George , Sakshi Joshi , Kaushal Santosh Bhogale , Deovrat Mehendale , Ishvinder Virender Sethi , Aparna Ananthanarayanan
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Hafsah Faquih Pratiti Palit Sneha Ravishankar Saranya Sukumaran Tripura Panchagnula Sunjay Murali Kunal Sharad Gandhi Ambujavalli R Manickam K M C Venkata Vaijayanthi Krishnan Srinivasa Raghavan Karunganni Pratyush Kumar Mitesh M Khapra
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classification cs.CL
keywords hoursdatadatasetindicvoiceslanguagestoolsblueprintcollection
verification ladder T0 review T1 audit T2 compute T3 formal
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We present INDICVOICES, a dataset of natural and spontaneous speech containing a total of 7348 hours of read (9%), extempore (74%) and conversational (17%) audio from 16237 speakers covering 145 Indian districts and 22 languages. Of these 7348 hours, 1639 hours have already been transcribed, with a median of 73 hours per language. Through this paper, we share our journey of capturing the cultural, linguistic and demographic diversity of India to create a one-of-its-kind inclusive and representative dataset. More specifically, we share an open-source blueprint for data collection at scale comprising of standardised protocols, centralised tools, a repository of engaging questions, prompts and conversation scenarios spanning multiple domains and topics of interest, quality control mechanisms, comprehensive transcription guidelines and transcription tools. We hope that this open source blueprint will serve as a comprehensive starter kit for data collection efforts in other multilingual regions of the world. Using INDICVOICES, we build IndicASR, the first ASR model to support all the 22 languages listed in the 8th schedule of the Constitution of India. All the data, tools, guidelines, models and other materials developed as a part of this work will be made publicly available

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Recognizing Every Voice: Towards Inclusive ASR for Rural Bhojpuri Women

    eess.AS 2025-06 conditional novelty 5.0 of 10

    Using 25-30 seconds of audio per speaker from 100 rural Bhojpuri women, synthetic speech augmentation cuts ASR word error on the new SRUTI benchmark by 4.7 points.

  2. HITSZ's End-To-End Speech Translation Systems Combining Sequence-to-Sequence Auto Speech Recognition Model and Indic Large Language Model for IWSLT 2025 in Indic Track

    cs.CL 2025-07 conditional novelty 4.0 of 10

    An end-to-end Whisper-plus-Krutrim speech translation system for English and Hindi, Bengali, and Tamil is evaluated on IWSLT 2025, with chain-of-thought fine-tuning showing large but selectively measured BLEU gains.

  3. Technical report: Impact of Duration Prediction on Speaker-specific TTS for Indian Languages

    eess.AS 2025-07 conditional novelty 4.0 of 10

    In a five-language zero-shot TTS study, no single duration prediction strategy dominates: speaker-prompted durations help some languages, infilling durations help others, and results vary by metric.

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