Audio-visual speaker recognition for video broadcast news

Audio-based speaker identification degrades severely when there is a mismatch between training and test conditions due either to channel or to noise. In this paper, we explore various techniques to combine video based speaker identification with audio-based speaker identification to improve the performance under mismatched conditions. Specifically, we explore techniques to optimally determine the relative weights of the independent decisions based on audio and video to achieve the best combination. Experiments on video broadcast news data show that significant improvements can be achieved by the fusion in acoustically degraded conditions.