mlsanityData to 7 Oct 2026

Improving Speech Recognition for Indic Languages using Language Model

arXiv 2203.16595 ↗First posted 30 Mar 2022cs.CL eess.AScited by 1

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Abstract (arXiv)

We study the effect of applying a language model (LM) on the output of Automatic Speech Recognition (ASR) systems for Indic languages. We fine-tune wav2vec 2.0 models for 18 Indic languages and adjust the results with language models trained on text derived from a variety of sources. Our findings demonstrate that the average Character Error Rate (CER) decreases by over 28 % and the average Word Error Rate (WER) decreases by about 36 % after decoding with LM. We show that a large LM may not provide a substantial improvement as compared to a diverse one. We also demonstrate that high quality transcriptions can be obtained on domain-specific data without retraining the ASR model and show results on biomedical domain.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 3 of its 8 references that are in the map. First posted on arXiv 30 Mar 2022.

Builds on

3 of 8 references.

References · 3

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  2. 2014Deep Speech: Scaling up end-to-end speech recognitioncited by 354
  3. 2021CLSRIL-23: Cross Lingual Speech Representations for Indic Languagescited by 22

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Citing papers per year: 2024: 1.

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  1. 2024Extracting Biomedical Entities from Noisy Audio Transcriptscited by 1