@inproceedings{braun2021on,
author = {Braun, Sebastian and Tashev, Ivan},
title = {On training targets for noise-robust voice activity detection},
booktitle = {European Signal Processing Conference (EUSIPCO)},
year = {2021},
month = {August},
abstract = {The task of voice activity detection (VAD) is an often required module in various speech processing, analysis and classification tasks. While state-of-the-art neural network based VADs can achieve great results, they often exceed computational budgets and real-time operating requirements. In this work, we propose a computationally efficient real-time VAD network that achieves state-of-the-art results on several public real recording datasets. We investigate different training targets for the VAD and show that using the segmental voice-to-noise ratio (VNR) is a better and more noise-robust training target than the clean speech level based VAD. We also show that multi-target training improves the performance further.},
url = {http://approjects.co.za/?big=en-us/research/publication/on-training-targets-for-noise-robust-voice-activity-detection/},
}