Improving Speaker Diarization of TV Series using Talking-Face Detection and Clustering

Abstract

While successful on broadcast news, meetings or telephone conversation, state-of-the-art speaker diarization techniques tend to perform poorly on TV series or movies. In this paper, we propose to rely on state-of-the-art face clustering techniques to guide acoustic speaker diarization. Two approaches are tested and evaluated on the first season of Game Of Thrones TV series. The second (better) approach relies on a novel talking-face detection module based on bi-directional long short-term memory recurrent neural network. Both audio-visual approaches outperform the audio-only baseline. A detailed study of the behavior of these approaches is also provided and paves the way to future improvements.