  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.
