Abstract
Voice Activity Detection (VAD) is an important pre-processing step in a wide variety of speech processing systems. VAD should in a practical application be able to detect speech in both noisy and noise-free environments, while not introducing significant latency. In this work we propose using an adversarial multi-task learning method when training a supervised VAD. The method has been applied to the state-of-the-art VAD Waveform-based Voice Activity Detection. Additionally the performance of the VAD is investigated under different algorithmic delays, which is an important factor in latency. Introducing adversarial multi-task learning to the model is observed to increase performance in terms of Area Under Curve (AUC), particularly in noisy environments, while the performance is not degraded at higher SNR levels. The adversarial multi-task learning is only applied in the training phase and thus introduces no additional
cost in testing. Furthermore the correlation between performance and algorithmic delays is investigated, and it is observed that the VAD performance degradation is only moderate when lowering the algorithmic delay from 398 ms to 23 ms. Index Terms: Voice Activity Detection, adversarial multi-task learning, algorithmic delay, deep learning, noise robustness.
cost in testing. Furthermore the correlation between performance and algorithmic delays is investigated, and it is observed that the VAD performance degradation is only moderate when lowering the algorithmic delay from 398 ms to 23 ms. Index Terms: Voice Activity Detection, adversarial multi-task learning, algorithmic delay, deep learning, noise robustness.
Original language | English |
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Title of host publication | Interspeech 2022 |
Publication date | 2022 |
Pages | 3759-3763 |
DOIs | |
Publication status | Published - 2022 |
Event | Interspeech 2022 - Incheon, Korea, Republic of Duration: 18 Sept 2022 → 22 Sept 2022 |
Conference
Conference | Interspeech 2022 |
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Country/Territory | Korea, Republic of |
City | Incheon |
Period | 18/09/2022 → 22/09/2022 |
Series | Proceedings of the International Conference on Spoken Language Processing |
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ISSN | 1990-9772 |