Stéphane H. Maes

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We describe our formulation of transformation enhanced data modeling used to develop a multi-grained data analysis approach to text independent speaker recognition. The broad goal is to address difficulties caused by sparse training and test data. First, our development of maximum likelihood transformation based recognition with diagonally constrained(More)
This paper presents a hierarchical approach to the Large-Scale Speaker Recognition problem. In here the authors present a binary tree database approach for arranging the trained speaker models based on a distance measure designed for comparing two sets of distributions. The combination of this hierarchical structure and the distance measure 1] provide the(More)
This paper presents a distance measure for evaluating the closeness of two sets of distributions. The job of nding the distance between two distributions has been addressed with many solutions present in the literature. To cluster speakers using the pre-computed models of their speech, a need arises for computing a distance between these models which are(More)
We identify a number of factors that may hinder the commercial success of location-based applications: the concern of privacy, the need to consider context beyond location, the presence of voluminous resources, and the constrained interfaces available on mobile devices. We describe an end-to-end system architecture with integrated support to address these(More)
| We present a transformation based, multi-grained data modeling technique in the context of text independent speaker recognition, aimed at mitigating diicul-ties caused by sparse training and test data. Both identi-cation and veriication are addressed, where we view the entire population as divided into the target population and its complement, which we(More)
This paper describes an implementation of the concept of conversational speech biometrics approach to personal authentication in telephony environment. An application independent module including a NL-enabled part for verbal veriication and identiication and an acoustic speaker recognition engine for voice-print analysis are combined using a special(More)
The paper presents results on speaker identification with a population size of over 10000 speakers. Speaker modeling is accomplished via our Transformation Enhanced Multi-Grained Models. Pursuing two goals, the first is to study the performance of a number of different systems within the modeling framework of multi-grained models. The second is to analyze(More)