  This paper presents a speech recognition system developed by the Transsion
Speech Understanding Processing Team (TSUP) for the ASRU 2023 MADASR Challenge.
The system focuses on adapting ASR models for low-resource Indian languages and
covers all four tracks of the challenge. For tracks 1 and 2, the acoustic model
utilized a squeezeformer encoder and bidirectional transformer decoder with
joint CTC-Attention training loss. Additionally, an external KenLM language
model was used during TLG beam search decoding. For tracks 3 and 4, pretrained
IndicWhisper models were employed and finetuned on both the challenge dataset
and publicly available datasets. The whisper beam search decoding was also
modified to support an external KenLM language model, which enabled better
utilization of the additional text provided by the challenge. The proposed
method achieved word error rates (WER) of 24.17%, 24.43%, 15.97%, and 15.97%
for Bengali language in the four tracks, and WER of 19.61%, 19.54%, 15.48%, and
15.48% for Bhojpuri language in the four tracks. These results demonstrate the
effectiveness of the proposed method.
