  Speech-to-text errors made by automatic speech recognition (ASR) systems
negatively impact downstream models. Error correction models as a
post-processing text editing method have been recently developed for refining
the ASR outputs. However, efficient models that meet the low latency
requirements of industrial grade production systems have not been well studied.
We propose PATCorrect-a novel non-autoregressive (NAR) approach based on
multi-modal fusion leveraging representations from both text and phoneme
modalities, to reduce word error rate (WER) and perform robustly with varying
input transcription quality. We demonstrate that PATCorrect consistently
outperforms state-of-the-art NAR method on English corpus across different
upstream ASR systems, with an overall 11.62% WER reduction (WERR) compared to
9.46% WERR achieved by other methods using text only modality. Besides, its
inference latency is at tens of milliseconds, making it ideal for systems with
low latency requirements.
