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SG-VAD: Stochastic Gates Based Speech Activity Detection

2022-10-28 09:50:59
Jonathan Svirsky, Ofir Lindenbaum
arXiv_SD
             
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. We publish the code and the models here this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2210.16022

PDF

https://arxiv.org/pdf/2210.16022.pdf

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