Personal VAD: Speaker-Conditioned Voice Activity Detection

Shaojin Ding, Quan Wang, Shuo-Yiin Chang, Li Wan, Ignacio Lopez Moreno


In this paper, we propose ""personal VAD"", a system to detect the voice activity of a target speaker at the frame level. This system is useful for gating the inputs to a streaming on-device speech recognition system, such that it only triggers for the target user, which helps reduce the computational cost and battery consumption, especially in scenarios where a keyword detector is unpreferable. We achieve this by training a VAD-alike neural network that is conditioned on the target speaker embedding or the speaker verification score. For each frame, personal VAD outputs the probabilities for three classes: non-speech, target speaker speech, and non-target speaker speech. Under our optimal setup, we are able to train a model with only 130K parameters that outperforms a baseline system where individually trained standard VAD and speaker recognition networks are combined to perform the same task.


 DOI: 10.21437/Odyssey.2020-62

Cite as: Ding, S., Wang, Q., Chang, S., Wan, L., Lopez Moreno, I. (2020) Personal VAD: Speaker-Conditioned Voice Activity Detection. Proc. Odyssey 2020 The Speaker and Language Recognition Workshop, 433-439, DOI: 10.21437/Odyssey.2020-62.


@inproceedings{Ding2020,
  author={Shaojin Ding and Quan Wang and Shuo-Yiin Chang and Li Wan and Ignacio  {Lopez Moreno}},
  title={{Personal VAD: Speaker-Conditioned Voice Activity Detection}},
  year=2020,
  booktitle={Proc. Odyssey 2020 The Speaker and Language Recognition Workshop},
  pages={433--439},
  doi={10.21437/Odyssey.2020-62},
  url={http://dx.doi.org/10.21437/Odyssey.2020-62}
}