arXiv
Open Access
2022
SG-VAD: Stochastic Gates Based Speech Activity Detection
Jonathan Svirsky
Ofir Lindenbaum
Abstrak
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 https://www.github.com/jsvir/vad.
Penulis (2)
J
Jonathan Svirsky
O
Ofir Lindenbaum
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2022
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓