Speaker verification is a biometric identity verification technique whose performance can be severely degraded by the presence of noise. Using a coherent notation, we reformulate and review several methods which have been proposed to quantify the uncertainty in verification results, some with a view to coping with the effects of mismatched training-testing environments. We also include a recent...

Statistics of frame-level pitch have recently been used in speaker recognition systems with good results [1, 2, 3]. Although they convey useful long-term information about a speaker’s distribution of f0 values, such statistics fail to capture information about local dynamics in intonation that characterize an individual’s speaking style. In this work, we take a first step toward capturing such ...

In this paper we present a study of the influence of alcohol intoxication on an automatic speaker verification system. It is widely known that alcohol intoxication affects one’s speech in many ways, but it remains to be studied how well a system can recognise a person affected by alcohol intoxication. Using the Alcohol Language Corpus as speech database and a text-independent GMM-UBM speaker ve...

The performance of a state-of-the-art speaker verification system is severely degraded when it is presented with trial recordings of short duration. In this work we propose to use deep neural networks to learn short-duration speaker embeddings. We focus on the 5s-5s condition, wherein both sides of a verification trial are 5 seconds long. In our previous work we established that learning a non-...

We mount an effective attack on a third-party open-source text-independent speaker verification system. Specifically, we show how an attacker can simply use a signal generated at fixed frequency to pass speaker verification and gain access to other user accounts. We demonstrate this attack on the GMM-UBM based ALIZE speaker verification system using the YOHO voice database. We show through expe...

The total variability based i-vector has become one of the most dominant approaches for speaker verification. In addition to this, recently the sparse representation (SR) based speaker verification approaches have also been proposed and are found to give comparable performance. In SR based approach, the dictionary used for sparse representation is either exemplar or learned from data using the ...

For speaker verification systems, security against imposture is one of the most important problems, and many approaches to reducing false acceptance of impostors as well as false rejection of clients have been investigated. On the other hand, imposture using synthetic speech has not been considered. In this paper, we investigate imposture against speaker verification systems using synthetic spe...

The performance of speaker verification system degrades when the test segments are utterances of short duration, therefore, we investigate the use of model representing our target speaker with his close speaker and his own speech data. We propose to create a new Speaker Model who groups close speakers (CS) achieved with two clustering algorithms in Automatic Speaker Verification A.S.V. Intra an...

This paper presents a new training procedure for speaker verification systems. The procedure extends previous speaker verification work by (1) developing a new discriminative a posteriori-based training algorithm, and (2) extending the algorithm to directly optimize speaker verification performance. The key features of the new training algorithm include leveraging current state of the art techn...

Speaker verification is an active research problem that has been addressed using a variety of different classification techniques. However, in general, methods inspired by the human auditory system tend to show better verification performance than other methods. In this thesis three biologically inspired speaker verification algorithms are presented. The first is a vowel-dependent speaker verif...