Name
Abstract | ||
|---|---|---|
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 speech recognition system, such that it only triggers for the target user, which helps reduce the computational cost and battery consumption. We achieve this by training a VAD-alike neural network that is conditioned on the target speaker embedding or the speaker verification score. For every frame, personal VAD outputs the scores for three classes: non-speech, target speaker speech, and non-target speaker speech. With our optimal setup, we are able to train a 130KB model that outperforms a baseline system where individually trained standard VAD and speaker recognition network are combined to perform the same task. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.21437/Odyssey.2020-62 | Odyssey |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ding Shaojin | 1 | 1 | 0.35 |
| Quan Wang | 2 | 115 | 20.15 |
| Shuo-Yiin Chang | 3 | 27 | 4.71 |
| Wan Li | 4 | 1 | 2.38 |
| Moreno Ignacio Lopez | 5 | 1 | 0.35 |