Abstract-Visual speech information from the speaker's mouth region has been successfully shown to improve noise robustness of automatic speech recognizers, thus promising to extend their usability into the human computer interface. In this paper, we review the main components of audio-visual automatic speech recognition and present novel contributions in two main areas: First, the visual front end design, based on a cascade of linear image transforms of an appropriate video region-of-interest, and subsequently, audio-visual speech integration. On the later topic, we discuss new work on feature and decision fusion combination, the modeling of audio-visual speech asynchrony, and incorporating modality reliability estimates to the bimodal recognition process. We also briefly touch upon the issue of audiovisual speaker adaptation. We apply our algorithms to three multi-subject bimodal databases, ranging from small-to largevocabulary recognition tasks, recorded at both visually controlled and challenging environments. Our experiments demonstrate that the visual modality improves automatic speech recognition over all conditions and data considered, however less so for visually challenging environments and large vocabulary tasks.

The most expressive way humans display emotions is through facial expressions. In

We present the use of layered probabilistic representations for modeling human activities, and describe how we use the representation to do sensing, learning, and inference at multiple levels of temporal granularity and abstraction and from heterogeneous data sources. The approach centers on the use of a cascade of Hidden Markov Models named Layered Hidden Markov Models (LHMMs) to diagnose states of a userÕs activity based on real-time streams of evidence from video, audio, and computer (keyboard and mouse) interactions. We couple these LHMMs with an expected utility analysis that considers the cost of misclassification. We describe the representation, present an implementation, and report on experiments with our layered architecture in a real-time office-awareness setting.

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Recognizing human facial expression and emotion by computer is an interesting and challenging problem. In this paper we propose a method for recognizing emotions through facial expressions displayed in video sequences. We introduce the Cauchy Naive Bayes classifier which uses the Cauchy distribution as the model distribution and we provide a framework for choosing the best model distribution assumption. Our person-dependent and person-independent experiments show that the Cauchy distribution assumption typically provides better results than the Gaussian distribution assumption.

The most expressive way humans display emotions is through facial expressions. In