SG-VAD: Stochastic Gates Based Speech Activity Detection

Jonathan Svirsky; Ofir Lindenbaum

doi:10.1109/icassp49357.2023.10096938

SG-VAD: Stochastic Gates Based Speech Activity Detection

Jonathan Svirsky, Ofir Lindenbaum

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

6 Scopus citations

Abstract

We propose a novel voice activity detection (VAD) model in a low-resource environment. Our key idea is to model VAD as a denoising task and construct a network that is designed to identify nuisance features for a speech classification task. We train the model to simultaneously identify irrelevant features while predicting the type of speech event. Our model contains only 7.8K parameters, outperforms the previously proposed methods on the AVA-Speech evaluation set, and provides comparative results on the HAVIC dataset. We present its architecture, experimental results, and ablation study on the model's components.

Original language	English
Title of host publication	ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728163277
DOIs	https://doi.org/10.1109/icassp49357.2023.10096938
State	Published - 2023
Event	48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2023-June
ISSN (Print)	1520-6149

Conference

Conference	48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Country/Territory	Greece
City	Rhodes Island
Period	4/06/23 → 10/06/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Feature Selection
Speech Recognition
Voice Activity Detection

Access to Document

10.1109/icassp49357.2023.10096938

Cite this

Svirsky, J., & Lindenbaum, O. (2023). SG-VAD: Stochastic Gates Based Speech Activity Detection. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2023-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/icassp49357.2023.10096938

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title = "SG-VAD: Stochastic Gates Based Speech Activity Detection",

abstract = "We propose a novel voice activity detection (VAD) model in a low-resource environment. Our key idea is to model VAD as a denoising task and construct a network that is designed to identify nuisance features for a speech classification task. We train the model to simultaneously identify irrelevant features while predicting the type of speech event. Our model contains only 7.8K parameters, outperforms the previously proposed methods on the AVA-Speech evaluation set, and provides comparative results on the HAVIC dataset. We present its architecture, experimental results, and ablation study on the model's components.",

keywords = "Feature Selection, Speech Recognition, Voice Activity Detection",

author = "Jonathan Svirsky and Ofir Lindenbaum",

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year = "2023",

doi = "10.1109/icassp49357.2023.10096938",

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series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

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Svirsky, J & Lindenbaum, O 2023, SG-VAD: Stochastic Gates Based Speech Activity Detection. in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2023-June, Institute of Electrical and Electronics Engineers Inc., 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023, Rhodes Island, Greece, 4/06/23. https://doi.org/10.1109/icassp49357.2023.10096938

SG-VAD: Stochastic Gates Based Speech Activity Detection. / Svirsky, Jonathan; Lindenbaum, Ofir.
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2023-June).

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

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AB - We propose a novel voice activity detection (VAD) model in a low-resource environment. Our key idea is to model VAD as a denoising task and construct a network that is designed to identify nuisance features for a speech classification task. We train the model to simultaneously identify irrelevant features while predicting the type of speech event. Our model contains only 7.8K parameters, outperforms the previously proposed methods on the AVA-Speech evaluation set, and provides comparative results on the HAVIC dataset. We present its architecture, experimental results, and ablation study on the model's components.

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Svirsky J, Lindenbaum O. SG-VAD: Stochastic Gates Based Speech Activity Detection. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/icassp49357.2023.10096938

SG-VAD: Stochastic Gates Based Speech Activity Detection

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