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Multimodal speech processing using asynchronous Hidden Markov Models
2004
Information Fusion
This paper advocates that for some multimodal tasks involving more than one stream of data representing the same sequence of events, it might sometimes be a good idea to be able to desynchronize the streams in order to maximize their joint likelihood. We thus present a novel Hidden Markov Model architecture to model the joint probability of pairs of asynchronous sequences describing the same sequence of events. An Expectation-Maximization algorithm to train the model is presented, as well as a
doi:10.1016/j.inffus.2003.04.001
fatcat:tzddmmp5s5d5pbmmghqy2xrzey