We propose a simple method for automatic speech recognition (ASR) by fine-tuning BERT, which is a language model (LM) trained on large-scale unlabeled text data and can generate rich contextual representations. Our assumption is that given a history context sequence, a powerful LM can narrow the range of possible choices and the speech signal can be used as a simple clue. Hence, comparing to conventional ASR systems that train a powerful acoustic model (AM) from scratch, we believe that speech recognition is possible by simply fine-tuning a BERT model. As an initial study, we demonstrate the effectiveness of the proposed idea on the AISHELL dataset and show that stacking a very simple AM on top of BERT can yield reasonable performance.
@article{arxiv.2102.00291,
title = {Speech Recognition by Simply Fine-tuning BERT},
author = {Wen-Chin Huang and Chia-Hua Wu and Shang-Bao Luo and Kuan-Yu Chen and Hsin-Min Wang and Tomoki Toda},
journal= {arXiv preprint arXiv:2102.00291},
year = {2021}
}