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    <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.</abstract>
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%0 Conference Proceedings
%T Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model
%A Jamshid Lou, Paria
%A Johnson, Mark
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F jamshid-lou-johnson-2017-disfluency
%X 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.
%R 10.18653/v1/P17-2087
%U https://aclanthology.org/P17-2087
%U https://doi.org/10.18653/v1/P17-2087
%P 547-553

Markdown (Informal)

[Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model](https://aclanthology.org/P17-2087) (Jamshid Lou & Johnson, ACL 2017)

• Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model (Jamshid Lou & Johnson, ACL 2017)

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.