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

ASR Error Correction and Domain Adaptation Using Machine Translation