This paper examines audio-visual speaker verification using a novel adaptation of fused hidden Markov models, in comparison to output fusion of individual classifiers in the audio and video modalities. A comparison of both hidden Markov model (HMM) and Gaussian mixture model (GMM) classifiers in both modalities under output fusion shows that the choice of audio classier is more important than video. Although temporal information allows a HMM to out-perform a GMM individually in video, this temporal information does not carry through to output fusion with an audio classier, where the difference between the two video classifiers is minor. An adaptation of fused hidden Markov models, designed to be more robust to within-speaker variation, is used to show that the temporal relationship between video observations and audio states can be harnessed to reduce errors in audio-visual speaker verification when compared to output fusion.

[ link | paper (pdf) | slides (ppt) ]

Fused hidden Markov models (FHMMs) have been shown to work well for the task of audio-visual speaker recognition, but only in an output decision-fusion configuration of both the audio- and video-biased versions of the FHMM structure. This paper looks at the performance of the audio and video-biased versions independently, and shows that the audio-biased version is considerably more capable for speaker recognition. Additionally, this paper shows that by taking advantage of the temporal relationship between the acoustic and visual data, the audio-biased FHMM provides better performance at less processing cost than best-performing output decision-fusion of regular HMMs.

[ link | paper (pdf) | slides (ppt) ]