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.- Anthology ID:
- 2020.findings-emnlp.186
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2051–2061
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.186
- DOI:
- 10.18653/v1/2020.findings-emnlp.186
- Cite (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.
- Cite (Informal):
- End-to-End Speech Recognition and Disfluency Removal (Jamshid Lou & Johnson, Findings 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.findings-emnlp.186.pdf
- Code
- pariajm/e2e-asr-and-disfluency-removal-evaluator + additional community code