ASR Error Correction and Domain Adaptation Using Machine Translation

• URL: http://arxiv.org/abs/2003.07692v1
• Date: Fri, 13 Mar 2020 20:05:38 GMT
• Title: ASR Error Correction and Domain Adaptation Using Machine Translation
• Authors: Anirudh Mani, Shruti Palaskar, Nimshi Venkat Meripo, Sandeep Konam, Florian Metze
• Abstract summary: We propose a technique to perform domain adaptation for ASR error correction via machine translation. We observe absolute improvement in word error rate and 4 point absolute improvement in BLEU score in Google ASR output.
• Score: 32.27379508770736
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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