Audio-visual speaker identification using coupled hidden Markov models

In this paper, we investigate the use of the coupled hidden Markov models (CHMM) for the task of audio-visual text dependent speaker identification. Our system determines the identity of the user from a temporal sequence of audio and visual observations obtained from the acoustic speech and the shape of the mouth, respectively. The multi modal observation sequences are then modeled using a set of CHMMs, one for each phoneme-viseme pair and for each person in the database. The use of CHMMs in our system is justified by the capacity of this model to describe the natural audio and visual state asynchrony as well as their conditional dependency over time. To train a CHMM we first train a speaker independent model using expectation-maximization (EM), and then we build a speaker dependent model using maximum a posteriori (MAP) training. Experimental results on XM2VTS database show that our system improves the accuracy of audio-only or video-only speaker identification at all levels of acoustic signal-to-noise ratio (SNR) from 0 to 30 dB.