Audio-visual speaker recognition for video broadcast news: some fusion techniques

Audio-based speaker identification degrades severely when there is a mismatch between training and test conditions either due to channel or noise. In this paper, we explore various techniques to fuse 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 suggest that significant improvements can be achieved by the combination in acoustically degraded conditions