# Improving Speech Recognition for Indic Languages using Language Model

arXiv: https://arxiv.org/abs/2203.16595 · HTML: https://mlsanity.com/paper/2203.16595/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 3 of 3)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [CLSRIL-23: Cross Lingual Speech Representations for Indic Languages](https://mlsanity.com/paper/2107.07402/) (2021)

## Built on by (most cited first, 1 of 1)

- [Extracting Biomedical Entities from Noisy Audio Transcripts](https://mlsanity.com/paper/2403.17363/) (2024)

## 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.