Off-the-shelf pre-trained Automatic Speech Recognition (ASR) systems are an increasingly viable service for companies of any size building speech-based products. While these ASR systems are trained on large amounts of data, domain mismatch is still an issue for many such parties that want to use this service as-is leading to not so optimal results for their task. We propose a simple technique to perform domain adaptation for ASR error correction via machine translation. The machine translation model is a strong candidate to learn a mapping from out-of-domain ASR errors to in-domain terms in the corresponding reference files. We use two off-the-shelf ASR systems in this work: Google ASR (commercial) and the ASPIRE model (open-source). We observe 7% absolute improvement in word error rate and 4 point absolute improvement in BLEU score in Google ASR output via our proposed method. We also evaluate ASR error correction via a downstream task of Speaker Diarization that captures speaker style, syntax, structure and semantic improvements we obtain via ASR correction.

本文提出了一种简单的技术，通过机器翻译实现ASR误差校正的领域自适应，我们使用了Google ASR和ASPIRE模型进行了实验，结果显示我们的方法可以使Google ASR输出的字错率下降7%，其BLEU分数绝对值提高了4点；同时，通过流下游任务Speaker Diarization，我们还评估了ASR误差校正，并捕捉了由ASR更正而获得的说话人风格、语法、结构和语义改进。

利用机器翻译进行ASR纠错和领域适应