Dynamic Stream Weight Modeling for Audio-Visual Speech Recognition

To generate optimal multi-stream audio-visual speech recognition performance, appropriate dynamic weighting of each modality is desired. In this paper, we propose to estimate such weights based on a combination of acoustic signal space observations and single-modality audio and visual speech model likelihoods. Two modeling approaches are investigated for such weight estimation: one based on a sigmoid fitting function, the other employing Gaussian mixture models. Reported experiments demonstrate that the later approach outperforms sigmoid based modeling, and is dramatically superior to the static weighting scheme.