Voice activity detection in noise?
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Voice activity detection (VAD) is an essential component in speech processing systems, especially in noisy environments with low signal-to-noise ratios (SNRs). Several approaches have been proposed to improve VAD performance in such conditions. One approach is to use video signals along with audio cues for VAD, as video signals are independent of background acoustic noise . Another approach is to use deep neural networks (DNNs) trained in a supervised manner, combined with adversarial domain adaptation to mitigate performance degradation due to background noises . Additionally, a computationally efficient real-time VAD network has been proposed, achieving state-of-the-art results by using the segmental voice-to-noise ratio (VNR) as a training target . Self-supervised learning has also been explored as a solution, improving training efficiency and testing performance in noise-corrupted datasets . Overall, these approaches aim to enhance VAD performance in noisy environments.
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| Papers (5) | Insight |
|---|---|
30 Aug 2021 1 Citations | The paper proposes a noise-tolerant self-supervised learning framework for audio-visual voice activity detection, which improves performance in noise-corrupted scenarios. |
Open access•Proceedings Article 01 Aug 2021 15 Citations | The paper proposes a computationally efficient real-time VAD network that achieves state-of-the-art results on several public real recording datasets. It investigates different training targets for VAD and shows that using the segmental voice-to-noise ratio (VNR) as a training target is more noise-robust than using the clean speech level. |
08 Jul 2021 3 Citations | The paper discusses the use of Voice Activity Detection (VAD) to detect speech activity in noisy environments. It uses energy and spectral centroid features to design VADs and evaluates their performance using F-score and Euclidean distance. |
23 May 2022 2 Citations | The paper proposes ADA-VAD, a deep neural network-based voice activity detection method that is robust to various noise types and low signal-to-noise ratios. |
01 Jan 2022 | The paper proposes a method for voice activity detection in noisy environments using visual cues extracted from video signals, which are independent of background acoustic noise. |
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