Improving Speech Recognition for Indic Languages using Language Model

• URL: http://arxiv.org/abs/2203.16595v1
• Date: Wed, 30 Mar 2022 18:22:12 GMT
• Title: Improving Speech Recognition for Indic Languages using Language Model
• Authors: Ankur Dhuriya, Harveen Singh Chadha, Anirudh Gupta, Priyanshi Shah, Neeraj Chhimwal, Rishabh Gaur, Vivek Raghavan
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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