A Unified Framework for Score Normalization Techniques Applied to Text Independent Speaker Verification

The purpose of this paper is to unify several of the state-of-the-art score normalization techniques applied to text-independent speaker verification systems. We propose a new framework for this purpose. The two well-known Z- and T-normalization techniques can be easily interpreted in this framework as different ways to estimate score distributions. This is useful as it helps to understand the various assumptions behind these well-known score normalization techniques, and opens the door for yet more complex solutions. Finally, some experiments on the Switchboard database are performed in order to illustrate the validity of the new proposed framework

A Kernel Trick For Sequences Applied to Text-Independent Speaker Verification Systems

This paper present a principled SVM based speaker verification system. We propose a new framework and a new sequence kernel that can make use of any Mercer kernel at the frame level. An extension of the sequence kernel based on the Max operator is also proposed. The new system is compared to state-of-the-art GMM and other SVM based systems found in the literature on the Banca and Polyvar databases. The new system outperforms, most of the time, the other systems, statistically significantly. Finally, the new proposed framework clarifies previous SVM based systems and suggests interesting future research directions

Estimating the quality of face localization for face verification

Face localization is the process of finding the exact position of a face in a given image. This can be useful in several applications such as face tracking or person authentication. The purpose of this paper is to show that the error made during the localization process may have different impacts depending on the final application. Hence in order to evaluate the performance of a face localization algorithm, we propose to embed the final application (here face verification) into the performance measuring process. Moreover, in this paper, we estimate this embedding using either a multilayer perceptron or a K nearest neighbor algorithm in order to speedup the evaluation process. We show on the BANCA database that our proposed measure best matches the final verification results when comparing several localization algorithms, on various performance measures currently used in face localization. 1