End-to-End Speech Recognition and Disfluency Removal

• URL: http://arxiv.org/abs/2009.10298v3
• Date: Mon, 28 Sep 2020 23:07:21 GMT
• Title: End-to-End Speech Recognition and Disfluency Removal
• Authors: Paria Jamshid Lou and Mark Johnson
• Abstract summary: This paper investigates the task of end-to-end speech recognition and disfluency removal. We show that end-to-end models do learn to directly generate fluent transcripts. We propose two new metrics that can be used for evaluating integrated ASR and disfluency models.
• Score: 15.910282983166024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Disfluency detection is usually an intermediate step between an automatic speech recognition (ASR) system and a downstream task. By contrast, this paper aims to investigate the task of end-to-end speech recognition and disfluency removal. We specifically explore whether it is possible to train an ASR model to directly map disfluent speech into fluent transcripts, without relying on a separate disfluency detection model. We show that end-to-end models do learn to directly generate fluent transcripts; however, their performance is slightly worse than a baseline pipeline approach consisting of an ASR system and a disfluency detection model. We also propose two new metrics that can be used for evaluating integrated ASR and disfluency models. The findings of this paper can serve as a benchmark for further research on the task of end-to-end speech recognition and disfluency removal in the future.

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