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    <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 specialized disfluency detection model. We also propose two new metrics for evaluating integrated ASR and disfluency removal 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.</abstract>
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%0 Conference Proceedings
%T End-to-End Speech Recognition and Disfluency Removal
%A Jamshid Lou, Paria
%A Johnson, Mark
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F jamshid-lou-johnson-2020-end
%X 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 specialized disfluency detection model. We also propose two new metrics for evaluating integrated ASR and disfluency removal 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.
%R 10.18653/v1/2020.findings-emnlp.186
%U https://aclanthology.org/2020.findings-emnlp.186
%U https://doi.org/10.18653/v1/2020.findings-emnlp.186
%P 2051-2061

Markdown (Informal)

[End-to-End Speech Recognition and Disfluency Removal](https://aclanthology.org/2020.findings-emnlp.186) (Jamshid Lou & Johnson, Findings 2020)

• End-to-End Speech Recognition and Disfluency Removal (Jamshid Lou & Johnson, Findings 2020)

ACL

• Paria Jamshid Lou and Mark Johnson. 2020. End-to-End Speech Recognition and Disfluency Removal. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2051–2061, Online. Association for Computational Linguistics.