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IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages
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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
Forward citations
Cited by 3 Pith papers
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Voice of India: A Large-Scale Benchmark for Real-World Speech Recognition in India
Voice of India is a new 536-hour benchmark of real telephonic conversations in 15 Indian languages with variant-aware transcripts for more realistic ASR evaluation.
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SamaVaani: Auditing and Debiasing Multilingual Clinical ASR for Indian Languages
Audit of multilingual clinical ASR reveals demographic biases; SamaVaani debiasing technique is proposed to jointly boost performance and fairness in Indian languages.
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Voice of India: A Large-Scale Benchmark for Real-World Speech Recognition in India
A 536-hour, 15-language, 139-cluster telephonic ASR benchmark for Indian languages with spelling-variation-aware transcripts and geographic performance analysis.
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