Target Active Speaker Detection with Audio-visual Cues

Abstract

In active speaker detection (ASD), we would like to detect whether an on-screen person is speaking based on audio-visual cues. Previous studies have primarily focused on modeling audio-visual synchronization cue, which depends on the video quality of the lip region of a speaker. In real-world applications, it is possible that we can also have the reference speech of the on-screen speaker. To benefit from both facial cue and reference speech, we propose the Target Speaker TalkNet (TS-TalkNet), which leverages a pre-enrolled speaker embedding to complement the audio-visual synchronization cue in detecting whether the target speaker is speaking. Our framework outperforms the popular model, TalkNet on two datasets, achieving absolute improvements of 1.6% in mAP on the AVA-ActiveSpeaker validation set, and 0.8%, 0.4%, and 0.8% in terms of AP, AUC and EER on the ASW test set, respectively. Code is available at https://github.com/Jiang-Yidi/TS-TalkNet/.