Guoqiang Li

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Maximum a posteriori adaptation method combines the prior knowledge with adaptation data from a new speaker, which has a nice asymptotical property, but has a slow adaptation rate for not modifying unseen models. In a strictly Bayesian approach, prior parameters are assumed known, based on common or subjective knowledge. But a practical solution is to adopt(More)
In a strictly Bayesian approach, prior parameters are assumed known, based on common or subjective knowledge. But a practical solution for maximum a posteriori adaptation methods is to adopt an empirical Bayesian approach, where the prior parameters are estimated directly from training speech data itself. So there is a problem of mismatches between training(More)
Visual feature extraction method now becomes the key technique in automatic speechreading systems. However it still remains a difficult problem due to large inter-person and intra-person appearance variabilities. In this paper, we extend the normal active shape model to a hierarchy probability-based framework, which can model a complex shape, such as human(More)
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