Accurately finding the wrong words in the automatic speech recognition (ASR) hypothesis and recovering them well-founded is the goal of speech error correction. In this paper, we propose a non-autoregressive speech error correction method. A Confidence Module measures the uncertainty of each word of the N-best ASR hypotheses as the reference to find the wrong word position. Besides, the acoustic feature from the ASR encoder is also used to provide the correct pronunciation references. N-best candidates from ASR are aligned using the edit path, to confirm each other and recover some missing character errors. Furthermore, the cross-attention mechanism fuses the information between error correction references and the ASR hypothesis. The experimental results show that both the acoustic and confidence references help with error correction. The proposed system reduces the error rate by 21% compared with the ASR model.

提出了一种非自回归的语音错误纠正方法，利用置信度模块和音频特征从自动语音识别假设中准确定位错误，采用交叉注意机制融合错误纠正参考和自动语音识别假设，并在实验中显示出音频和置信度参考能够帮助错误修正，该系统与自动语音识别模型相比减少了21%的错误率。

通过同时关注声音和置信度引用进行自动语音识别的错误修正