This paper presents an overview of a state-of-the-art text-independent speaker verification system. First, an introduction proposes a modular scheme of the training and test phases of a speaker verification system. Then, the most commonly speech parameterization used in speaker verification, namely, cepstral analysis, is detailed. Gaussian mixture modeling, which is the speaker modeling technique used in most systems, is then explained. A few speaker modeling alternatives, namely, neural networks and support vector machines, are mentioned. Normalization of scores is then explained, as this is a very important step to deal with real-world data. The evaluation of a speaker verification system is then detailed, and the detection error trade-off (DET) curve is explained. Several extensions of speaker verification are then enumerated, including speaker tracking and segmentation by speakers. Then, some applications of speaker verification are proposed, including on-site applications, remote applications, applications relative to structuring audio information, and games. Issues concerning the forensic area are then recalled, as we believe it is very important to inform people about the actual performance and limitations of speaker verification systems. This paper concludes by giving a few research trends in speaker verification for the next couple of years.

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.