Integrated Semantic and Phonetic Post-correction for Chinese Speech Recognition

Yi-Chang Chen, Chun-Yen Cheng, Chien-An Chen, Ming-Chieh Sung, Yi-Ren Yeh

Abstract
Due to the recent advances of natural language processing, several works have applied the pre-trained masked language model (MLM) of BERT to the post-correction of speech recognition. However, existing pre-trained models only consider the semantic correction while the phonetic features of words is neglected. The semantic-only post-correction will consequently decrease the performance since homophonic errors are fairly common in Chinese ASR. In this paper, we proposed a novel approach to collectively exploit the contextualized representation and the phonetic information between the error and its replacing candidates to alleviate the error rate of Chinese ASR. Our experiment results on real world speech recognition datasets showed that our proposed method has evidently lower CER than the baseline model, which utilized a pre-trained BERT MLM as the corrector.
Anthology ID:
2021.rocling-1.13
Volume:
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
Month:
October
Year:
2021
Address:
Taoyuan, Taiwan
Venue:
ROCLING
SIG:
Publisher:
The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Note:
Pages:
95–102
Language:
URL:
https://aclanthology.org/2021.rocling-1.13
DOI:
Bibkey:
Cite (ACL):
Yi-Chang Chen, Chun-Yen Cheng, Chien-An Chen, Ming-Chieh Sung, and Yi-Ren Yeh. 2021. Integrated Semantic and Phonetic Post-correction for Chinese Speech Recognition. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 95–102, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).
Cite (Informal):
Integrated Semantic and Phonetic Post-correction for Chinese Speech Recognition (Chen et al., ROCLING 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/starsem-semeval-split/2021.rocling-1.13.pdf
Code
 gitycc/phonetic_mlm
Data
AISHELL-3
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