Boosting Chinese ASR Error Correction with Dynamic Error Scaling
  Mechanism
Jiaxin Fan Yong Zhang Hanzhang Li Jianzong Wang Zhitao Li Sheng Ouyang Ning Cheng Jing Xiao
Chinese Automatic Speech Recognition (ASR) error correction presents
significant challenges due to the Chinese language's unique features, including
a large character set and borderless, morpheme-based structure. Current
mainstream models often struggle with effectively utilizing word-level features
and phonetic information. This paper introduces a novel approach that
incorporates a dynamic error scaling mechanism to detect and correct
phonetically erroneous text generated by ASR output. This mechanism operates by
dynamically fusing word-level features and phonetic information, thereby
enriching the model with additional semantic data. Furthermore, our method
implements unique error reduction and amplification strategies to address the
issues of matching wrong words caused by incorrect characters. Experimental
results indicate substantial improvements in ASR error correction,
demonstrating the effectiveness of our proposed method and yielding promising
results on established datasets.