Disfluency detection using a noisy channel model and a deep neural language model

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationACL 2017 - The 55th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publicationProceedings of the Conference, Vol. 2 (Short Papers)
EditorsRegina Barzilay, Min-Yen Kan
Place of PublicationStroudsburg PA
PublisherAssociation for Computational Linguistics (ACL)
Pages547-553
Number of pages7
Volume2
ISBN (Electronic)9781945626760
DOIs
Publication statusPublished - 2017
Event55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 - Vancouver, Canada
Duration: 30 Jul 20174 Aug 2017

Conference

Conference55th Annual Meeting of the Association for Computational Linguistics, ACL 2017
CountryCanada
CityVancouver
Period30/07/174/08/17

Bibliographical note

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  • Cite this

    Jamshid Lou, P., & Johnson, M. (2017). Disfluency detection using a noisy channel model and a deep neural language model. In R. Barzilay, & M-Y. Kan (Eds.), ACL 2017 - The 55th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, Vol. 2 (Short Papers) (Vol. 2, pp. 547-553). Stroudsburg PA: Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/P17-2087