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

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

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.

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)
Place of PublicationStroudsburg PA
PublisherAssociation for Computational Linguistics (ACL)
Pages547-553
Number of pages7
Volume2
ISBN (Electronic)9781945626760
DOIs
Publication statusPublished - 1 Jan 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

Fingerprint

language
Language Model
Disfluency
Short-term Memory
candidacy
Long short-term memory
Maximum Entropy
Spontaneous Speech

Cite this

Jamshid Lou, P., & Johnson, M. (2017). Disfluency detection using a noisy channel model and a deep neural language model. In 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
Jamshid Lou, Paria ; Johnson, Mark. / Disfluency detection using a noisy channel model and a deep neural language model. ACL 2017 - The 55th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, Vol. 2 (Short Papers). Vol. 2 Stroudsburg PA : Association for Computational Linguistics (ACL), 2017. pp. 547-553
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Jamshid Lou, P & Johnson, M 2017, Disfluency detection using a noisy channel model and a deep neural language model. in ACL 2017 - The 55th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, Vol. 2 (Short Papers). vol. 2, Association for Computational Linguistics (ACL), Stroudsburg PA, pp. 547-553, 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 30/07/17. https://doi.org/10.18653/v1/P17-2087

Disfluency detection using a noisy channel model and a deep neural language model. / Jamshid Lou, Paria; Johnson, Mark.

ACL 2017 - The 55th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, Vol. 2 (Short Papers). Vol. 2 Stroudsburg PA : Association for Computational Linguistics (ACL), 2017. p. 547-553.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

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