Audiovisual-based adaptive speaker identification

Abstract

An adaptive speaker identification system is presented in this paper, which aims to recognize speakers in feature films by exploiting both audio and visual cues. Specifically, the audio source is first analyzed to identify speakers using a likelihood-based approach. Meanwhile, the visual source is parsed to recognize talking faces using face detection/recognition and mouth tracking techniques. These two information sources are then integrated under a probabilistic framework for improved system performance. Moreover, to account for speakers' voice variations along time, we update their acoustic models on the fly by adapting to their newly contributed speech data. An average of 80% identification accuracy has been achieved on two test movies. This shows a promising future for the proposed audiovisual-based adaptive speaker identification approach.