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arXiv · 2312.14860

Advancing VAD Systems Based on Multi-Task Learning with Improved Model Structures

Lingyun Zuo·Keyu An·Shiliang Zhang·Zhijie Yan
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Abstract

In a speech recognition system, voice activity detection (VAD) is a crucial frontend module. Addressing the issues of poor noise robustness in traditional binary VAD systems based on DFSMN, the paper further proposes semantic VAD based on multi-task learning with improved models for real-time and offline systems, to meet specific application requirements. Evaluations on internal datasets show that, compared to the real-time VAD system based on DFSMN, the real-time semantic VAD system based on RWKV achieves relative decreases in CER of 7.0\%, DCF of 26.1\% and relative improvement in NRR of 19.2\%. Similarly, when compared to the offline VAD system based on DFSMN, the offline VAD system based on SAN-M demonstrates relative decreases in CER of 4.4\%, DCF of 18.6\% and relative improvement in NRR of 3.5\%.

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Lingyun Zuo, Keyu An, Shiliang Zhang, Zhijie Yan. 2023-12-19. Advancing VAD Systems Based on Multi-Task Learning with Improved Model Structures. https://arxiv.org/abs/2312.14860

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