The task of voice activity detection (VAD) is an often required module in various speech processing, analysis and classi\ufb01cation 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 ef\ufb01cient 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
The task of voice activity detection (VAD) is an often required module in various speech processing, analysis and classi\ufb01cation 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 ef\ufb01cient real-time VAD network that achieves state-of-the-art results on 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