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SPRING-INX: A Multilingual Indian Language Speech Corpus by SPRING Lab, IIT Madras

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arxiv 2310.14654 v2 pith:MX5MNRF3 submitted 2023-10-23 cs.CL eess.AS

Nithya R , Malavika S , Jordan F , Arjun Gangwar , Metilda N J , S Umesh , Rithik Sarab , Akhilesh Kumar Dubey
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Govind Divakaran Samudra Vijaya K Suryakanth V Gangashetty
This is my paper
classification cs.CLeess.AS
keywords dataindianlanguagesspeechlanguagetechnologyapplicationsbuilding
verification ladder T0 review T1 audit T2 compute T3 formal
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India is home to a multitude of languages of which 22 languages are recognised by the Indian Constitution as official. Building speech based applications for the Indian population is a difficult problem owing to limited data and the number of languages and accents to accommodate. To encourage the language technology community to build speech based applications in Indian languages, we are open sourcing SPRING-INX data which has about 2000 hours of legally sourced and manually transcribed speech data for ASR system building in Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi and Tamil. This endeavor is by SPRING Lab , Indian Institute of Technology Madras and is a part of National Language Translation Mission (NLTM), funded by the Indian Ministry of Electronics and Information Technology (MeitY), Government of India. We describe the data collection and data cleaning process along with the data statistics in this paper.

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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. NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A real-world continual learning benchmark for multilingual ASR built from 3,250 hours of Indian language speech shows that no current CL method performs consistently across language- and domain-incremental scenarios.

  2. 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.

  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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