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

Synchronous Alignment

In speaker verification, the maximum Likelihood between criterion is generally used to verify the claimed identity. This is done using two independent models, i.e. a Client model and a World model. It may be interesting to make both models share the same topology, which represent the phonetic underlying structure, and then to consider two different output distributions corresponding to the Client/World hypotheses. Based on this idea, a decoding algorithm and the corresponding training algorithm were derived. The first experiments show, on a significant telephone database, a small improvement with respect to the reference system, we can conclude that at least synchronous alignment provides equivalent results to the reference system with a reduced complexity decoding algorithm. Other important perspectives can be derived

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

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

Adaptation robuste de modeles HMM pour la verification du locuteur dependante du texte

When deploying a secure system based on speaker verification, the limited amount of training data is usually critical. Indeed, the enrollment procedure must be fast and user-friendly. An incremental training of HMM speaker models, based on a MAP (Maximum A Posteriori) adaptation technique is used in order to make the enrollment more robust with only one or two utterances of the client password. This paper presents the improvements which can be achieved, in term of verification performance and stability of the decision thresholds. Our results highlight the benefits of MAP adaptation in conjunction with a synchronous alignment approach