  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.

Computation and Language;Audio and Speech Processing