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Multilingual and code-switching ASR challenges for low resource Indian languages

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arxiv 2104.00235 v1 pith:B5ZCPDO6 submitted 2021-04-01 cs.CL eess.AS

Anuj Diwan , Rakesh Vaideeswaran , Sanket Shah , Ankita Singh , Srinivasa Raghavan , Shreya Khare , Vinit Unni , Saurabh Vyas
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Akash Rajpuria Chiranjeevi Yarra Ashish Mittal Prasanta Kumar Ghosh Preethi Jyothi Kalika Bali Vivek Seshadri Sunayana Sitaram Samarth Bharadwaj Jai Nanavati Raoul Nanavati Karthik Sankaranarayanan Tejaswi Seeram Basil Abraham
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classification cs.CLeess.AS
keywords languagescode-switchingmultilingualmultiplespeechbuildingdataincreasing
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Recently, there is increasing interest in multilingual automatic speech recognition (ASR) where a speech recognition system caters to multiple low resource languages by taking advantage of low amounts of labeled corpora in multiple languages. With multilingualism becoming common in today's world, there has been increasing interest in code-switching ASR as well. In code-switching, multiple languages are freely interchanged within a single sentence or between sentences. The success of low-resource multilingual and code-switching ASR often depends on the variety of languages in terms of their acoustics, linguistic characteristics as well as the amount of data available and how these are carefully considered in building the ASR system. In this challenge, we would like to focus on building multilingual and code-switching ASR systems through two different subtasks related to a total of seven Indian languages, namely Hindi, Marathi, Odia, Tamil, Telugu, Gujarati and Bengali. For this purpose, we provide a total of ~600 hours of transcribed speech data, comprising train and test sets, in these languages including two code-switched language pairs, Hindi-English and Bengali-English. We also provide a baseline recipe for both the tasks with a WER of 30.73% and 32.45% on the test sets of multilingual and code-switching subtasks, respectively.

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Cited by 5 Pith papers

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

  1. SraVaani 1.0: Scaling Inclusive Speech Recognition for Indic Languages

    eess.AS 2026-08 conditional novelty 6.0 of 10

    SraVaani-1.0 reports the broadest open ASR coverage for Indic languages to date, with competitive word error rates on 17 benchmark languages and the only transcription output for 44 low-resource languages, evaluated o...

  2. Dhvani: A Weakly-supervised Phonemic Error Detection and Personalized Feedback System for Hindi

    eess.AS 2025-06 reject novelty 5.0 of 10

    Dhvani adapts a weakly-supervised model to Hindi pronunciation error detection using synthetic mispronunciations, reporting 82% F1 on the synthetic test set but no validation on real non-native speech.

  3. Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR

    cs.CL 2026-07 conditional novelty 4.0 of 10

    Iterative pseudo-labeling on 22.4k hours of unlabeled code-switching audio reduces Mix Error Rate on SEAME devman to 12.88% and devsge to 18.89%.

  4. Technical Report: A Practical Guide to Kaldi ASR Optimization

    cs.SD 2025-06 reject novelty 3.0 of 10

    The paper proposes engineering tweaks to Kaldi ASR (Conformer+TDNN-F architecture, SpecAugment, Bayesian n-gram merging) but presents no experimental evidence for any claimed improvement.

  5. From Statistical Methods to Pre-Trained Models; A Survey on Automatic Speech Recognition for Resource Scarce Urdu Language

    cs.CL 2024-11 conditional novelty 3.0 of 10

    A survey of Urdu ASR that catalogs datasets, toolkits, and techniques from HMM-GMM to pre-trained multilingual models, concluding that data scarcity and limited transfer learning hold the field back.

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