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.

利用动态错误放大机制来检测和纠正自动语音识别输出中的语音错误，通过动态融合单词级特征和语音信息，丰富模型的句义数据，并实施唯一的错误减少和放大策略，以解决因错误字符导致匹配错误单词的问题，实验结果表明在 ASR 错误修正方面取得了重大改进，证明了我们提出方法的有效性，并在已建立的数据集中取得了有希望的结果。