A Lightweight Framework for Online Voice Activity Detection in the Wild
(3 minutes introduction)
| Xuenan Xu (SJTU, China), Heinrich Dinkel (Xiaomi, China), Mengyue Wu (SJTU, China), Kai Yu (SJTU, China) |
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Voice activity detection (VAD) is an essential pre-processing component for speech-related tasks such as automatic speech recognition (ASR). Traditional VAD systems require strong frame-level supervision for training, inhibiting their performance in real-world test scenarios. Previously, the general-purpose VAD (GPVAD) framework has been proposed to enhance noise robustness significantly. However, GPVAD models are comparatively large and only work for offline evaluation. This work proposes the use of a knowledge distillation framework, where a (large, offline) teacher model provides frame-level supervision to a (light, online) student model. Our experiments verify that our proposed lightweight student models outperform GPVAD on all test sets, including clean, synthetic and real-world scenarios. Our smallest student model only uses 2.2% of the parameters and 15.9% duration cost of our teacher model for inference when evaluated on a Raspberry Pi.
