Although over 300M around the world speak Bangla, scant work has been done in
improving Bangla voice-to-text transcription due to Bangla being a low-resource
language. However, with the introduction of the Bengali Common Voice 9.0 speech
dataset, Automatic Speech Recognition (ASR) models can now be significantly
improved. With 399hrs of speech recordings, Bengali Common Voice is the largest
and most diversified open-source Bengali speech corpus in the world. In this
paper, we outperform the SOTA pretrained Bengali ASR models by finetuning a
pretrained wav2vec2 model on the common voice dataset. We also demonstrate how
to significantly improve the performance of an ASR model by adding an n-gram
language model as a post-processor. Finally, we do some experiments and
hyperparameter tuning to generate a robust Bangla ASR model that is better than
the existing ASR models.

通过微调波形向量预训练模型，并将 N-gram 语言模型作为后处理程序，我们在 Bengali Common Voice 语音数据集上生成了一个性能更好的孟加拉语自动语音识别模型，并且比现有模型更具有鲁棒性。