Post-editing in Automatic Speech Recognition (ASR) entails automatically
correcting common and systematic errors produced by the ASR system. The outputs
of an ASR system are largely prone to phonetic and spelling errors. In this
paper, we propose to use a powerful pre-trained sequence-to-sequence model,
BART, further adaptively trained to serve as a denoising model, to correct
errors of such types. The adaptive training is performed on an augmented
dataset obtained by synthetically inducing errors as well as by incorporating
actual errors from an existing ASR system. We also propose a simple approach to
rescore the outputs using word level alignments. Experimental results on
accented speech data demonstrate that our strategy effectively rectifies a
significant number of ASR errors and produces improved WER results when
compared against a competitive baseline. We also highlight a negative result
obtained on the related grammatical error correction task in Hindi language
showing the limitation in capturing wider context by our proposed model.

该研究使用预训练序列到序列模型 BART，并对其进行自适应训练来对自动语音识别中的发音和拼写错误进行纠正，并采用词级对齐的简单方法重新评分。实验结果表明，该策略在口音语音数据上有效地矫正了大量 ASR 错误，并在与基准系统的比较中产生了改进的 WER 结果。然而，在印地语语法错误纠正任务中，该模型捕捉更广泛上下文的能力受到限制。