2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDHLT-1.3
Paper Title SPEECH RECOGNITION BY SIMPLY FINE-TUNING BERT
Authors Wen-Chin Huang, Nagoya University, Japan; Chia-Hua Wu, Shang-Bao Luo, Academia Sinica, Taiwan; Kuan-Yu Chen, National Taiwan University of Science and Technology, Taiwan; Hsin-Min Wang, Academia Sinica, Taiwan; Tomoki Toda, Nagoya University, Japan
SessionHLT-1: Language Modeling 1: Fusion and Training for End-to-End ASR
LocationGather.Town
Session Time:Tuesday, 08 June, 13:00 - 13:45
Presentation Time:Tuesday, 08 June, 13:00 - 13:45
Presentation Poster
Topic Speech Processing: [SPE-GASR] General Topics in Speech Recognition
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract 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.