SPRING-INX: A Multilingual Indian Language Speech Corpus by SPRING Lab, IIT Madras
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 2 of its 3 references that are in the map. First posted on arXiv 23 Oct 2023.
Builds on
2 of 3 references.
References · 2
- 2017Attention Is All You Needcited by 59,932
- 2018ESPnet: End-to-End Speech Processing Toolkitcited by 554
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Citing papers per year: 2024: 2; 2025: 1; 2026: 6.
9 papers.
- 2024LAHAJA: A Robust Multi-accent Benchmark for Evaluating Hindi ASR Systemscited by 8
- 2024Empowering Low-Resource Language ASR via Large-Scale Pseudo Labelingcited by 4
- 2025NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Datacited by 2
- 2026Benchmarking Automatic Speech Recognition for Indian Languages in Agricultural Contextscited by 2
- 2026Breaking the Script Barrier: Enabling Automatic Alignment for PoS-based ASR Error Analysis in Non-Latin Scriptscited by 0
- 2026Multilingual Multi-Speaker Unit Vocoders: A Systematic Analysis of Discrete Speech Representationscited by 0
- 2026TamilEOT: A Dataset and Model for Semantic End-of-Turn Detection in Tamil Telephone Speechcited by 0
- 2026Model-Agnostic and Language-Agnostic Voice Pipeline Improvement for the Agriculture Domaincited by 0
- 2026Vimarsha: Faithful ASR Evaluation for Indian Languages with Demographic Diversity, In-the-Wild Audio and Spelling Variationscited by 0
- 2026Vimarsha: Faithful ASR Evaluation for Indian Languages with Demographic Diversity, In-the-Wild Audio and Spelling Variations21 Sep 2026
- 2026Model-Agnostic and Language-Agnostic Voice Pipeline Improvement for the Agriculture Domain17 Sep 2026
- 2026TamilEOT: A Dataset and Model for Semantic End-of-Turn Detection in Tamil Telephone Speech4 Sep 2026
- 2026Multilingual Multi-Speaker Unit Vocoders: A Systematic Analysis of Discrete Speech Representations4 Jun 2026
- 2026Breaking the Script Barrier: Enabling Automatic Alignment for PoS-based ASR Error Analysis in Non-Latin Scripts27 May 2026
- 2026Benchmarking Automatic Speech Recognition for Indian Languages in Agricultural Contexts31 Jan 2026
- 2025NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data1 Jul 2025
- 2024Empowering Low-Resource Language ASR via Large-Scale Pseudo Labeling26 Aug 2024
- 2024LAHAJA: A Robust Multi-accent Benchmark for Evaluating Hindi ASR Systems21 Aug 2024