How do voice activity detection algorithms improve noise-robust speech processing?
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Voice activity detection (VAD) algorithms enhance noise-robust speech processing by accurately identifying speech regions amidst various noise types and low signal-to-noise ratios. Various approaches have been proposed to improve VAD performance. For instance, the adversarial domain adaptive VAD (ADA-VAD) utilizes deep neural networks to robustly detect speech in noisy backgrounds . Additionally, a computationally efficient real-time VAD network achieves state-of-the-art results by using the segmental voice-to-noise ratio as a noise-robust training target . Moreover, pre-processing methods like beamforming and spatial target speaker detection significantly enhance single-channel VAD algorithms, outperforming multi-channel VAD in challenging noise conditions . These advancements in VAD technology contribute to more accurate and reliable speech processing in noisy environments.
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| Papers (5) | Insight |
|---|---|
1 Citations | Voice activity detection algorithms enhance noise-robust speech processing by accurately distinguishing speech from non-speech regions, reducing misclassification errors, and achieving high detection rates even in low SNR levels. |
Open access•Posted Content | Voice activity detection algorithms improve noise-robust speech processing by using segmental voice-to-noise ratio (VNR) as a training target, enhancing performance, and achieving state-of-the-art results in real-time applications. |
Open access•Posted Content 12 Apr 2021 | Single-channel voice activity detection algorithms can be enhanced with spatial pre-processing techniques like beamforming and spatial target speaker detection, resulting in superior noise-robust speech processing. |
23 May 2022 2 Citations | Voice activity detection algorithms improve noise-robust speech processing by utilizing adversarial domain adaptation to match noisy and clean audio domains, enhancing robustness to various noise types and low SNRs. |
Open access•Proceedings Article 01 Aug 2021 15 Citations | Voice activity detection algorithms improve noise-robust speech processing by using segmental voice-to-noise ratio (VNR) as a training target, enhancing performance, and achieving state-of-the-art results in real-time applications. |
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