Joint, Incremental Disfluency Detection and Utterance Segmentation from Speech

Julian Hough, David Schlangen


Abstract
We present the joint task of incremental disfluency detection and utterance segmentation and a simple deep learning system which performs it on transcripts and ASR results. We show how the constraints of the two tasks interact. Our joint-task system outperforms the equivalent individual task systems, provides competitive results and is suitable for future use in conversation agents in the psychiatric domain.
Anthology ID:
E17-1031
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
326–336
Language:
URL:
https://aclanthology.org/E17-1031
DOI:
Bibkey:
Cite (ACL):
Julian Hough and David Schlangen. 2017. Joint, Incremental Disfluency Detection and Utterance Segmentation from Speech. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 326–336, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Joint, Incremental Disfluency Detection and Utterance Segmentation from Speech (Hough & Schlangen, EACL 2017)
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PDF:
https://aclanthology.org/E17-1031.pdf