Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model

Paria Jamshid Lou, Mark Johnson


Abstract
This paper presents a model for disfluency detection in spontaneous speech transcripts called LSTM Noisy Channel Model. The model uses a Noisy Channel Model (NCM) to generate n-best candidate disfluency analyses and a Long Short-Term Memory (LSTM) language model to score the underlying fluent sentences of each analysis. The LSTM language model scores, along with other features, are used in a MaxEnt reranker to identify the most plausible analysis. We show that using an LSTM language model in the reranking process of noisy channel disfluency model improves the state-of-the-art in disfluency detection.
Anthology ID:
P17-2087
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
547–553
Language:
URL:
https://aclanthology.org/P17-2087
DOI:
10.18653/v1/P17-2087
Bibkey:
Cite (ACL):
Paria Jamshid Lou and Mark Johnson. 2017. Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 547–553, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model (Jamshid Lou & Johnson, ACL 2017)
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PDF:
https://aclanthology.org/P17-2087.pdf