Integrated Semantic and Phonetic Post-correction for Chinese Speech Recognition
This addresses a specific issue in Chinese ASR for users needing more accurate transcriptions, though it is incremental as it builds on existing post-correction methods.
The paper tackled the problem of homophonic errors in Chinese speech recognition by integrating phonetic information with semantic post-correction, resulting in a lower character error rate (CER) compared to a baseline using a pre-trained BERT model.
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.