Blind phoneme segmentation with temporal prediction errors

Paul Michel; Okko Räsänen; Roland Thiolliere; Emmanuel Dupoux

doi:10.18653/v1/P17-3011

Blind phoneme segmentation with temporal prediction errors

Paul Michel, Okko Räsänen, Roland Thiolliere, Emmanuel Dupoux

Department of Signal Processing and Acoustics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

2 Citations (Scopus)

152 Downloads (Pure)

Abstract

Phonemic segmentation of speech is a critical step of speech recognition systems. We propose a novel unsupervised algorithm based on sequence prediction models such as Markov chains and recurrent neural networks. Our approach consists in analyzing the error profile of a model trained to predict speech features frame-by-frame. Specifically, we try to learn the dynamics of speech in the MFCC space and hypothesize boundaries from local maxima in the prediction error. We evaluate our system on the TIMIT dataset, with improvements over similar methods.

Original language	English
Title of host publication	Proceedings of the Student Research Workshop at the Annual Meeting of the Association for Computational Linguistics
Pages	62-68
Number of pages	7
ISBN (Electronic)	978-1-945626-56-2
DOIs	https://doi.org/10.18653/v1/P17-3011
Publication status	Published - 2017
MoE publication type	A4 Article in a conference publication
Event	Annual Meeting of the Association for Computational Linguistics: Student Research Workshop - Vancouver, Canada Duration: 30 Jul 2017 → 4 Aug 2017

Conference

Conference	Annual Meeting of the Association for Computational Linguistics
Abbreviated title	SRW
Country	Canada
City	Vancouver
Period	30/07/2017 → 04/08/2017

Access to Document

10.18653/v1/P17-3011Licence: CC BY

ELEC-Michel-et-al-Blind phoneme_ACL2017Final published version, 696 KBLicence: CC BY

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Cite this

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title = "Blind phoneme segmentation with temporal prediction errors",

abstract = "Phonemic segmentation of speech is a critical step of speech recognition systems. We propose a novel unsupervised algorithm based on sequence prediction models such as Markov chains and recurrent neural networks. Our approach consists in analyzing the error profile of a model trained to predict speech features frame-by-frame. Specifically, we try to learn the dynamics of speech in the MFCC space and hypothesize boundaries from local maxima in the prediction error. We evaluate our system on the TIMIT dataset, with improvements over similar methods.",

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language = "English",

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booktitle = "Proceedings of the Student Research Workshop at the Annual Meeting of the Association for Computational Linguistics",

note = "Annual Meeting of the Association for Computational Linguistics : Student Research Workshop, SRW ; Conference date: 30-07-2017 Through 04-08-2017",

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Michel, P, Räsänen, O, Thiolliere, R & Dupoux, E 2017, Blind phoneme segmentation with temporal prediction errors. in Proceedings of the Student Research Workshop at the Annual Meeting of the Association for Computational Linguistics. pp. 62-68, Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, 30/07/2017. https://doi.org/10.18653/v1/P17-3011

Blind phoneme segmentation with temporal prediction errors. / Michel, Paul; Räsänen, Okko; Thiolliere, Roland; Dupoux, Emmanuel.

Proceedings of the Student Research Workshop at the Annual Meeting of the Association for Computational Linguistics. 2017. p. 62-68.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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DO - 10.18653/v1/P17-3011

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