In the literature, much research work has been done in the area of speaker verification. The developments include: different types of speaker verification techniques, methods for feature extraction, measures for telephone channel compensation, system robustness etc. In contrast, the problem of acoustic feature selection for speaker verification has been relatively neglected. Hence our aim is to...

We developed a new speaker verification system that is robust to intra-speaker variation. There is a strong likelihood that intraspeaker variations will occur due to changes in talking styles, the periods when an individual speaks, and so on. It is well known that such variation generally degrades the performance of speaker verification systems. To solve this problem, we applied multiple kernel...

In this paper, we propose the fusion of audio and explicit lip motion features for speaker identity verification applications. Experimental results using GMM-based speaker models indicate that audiovisual fusion with explicit lip motion information provides significant performance improvement for verifying both the speaker identity and the liveness, due to tracking of the closely coupled acoust...

In this paper, we examine the problem of quality measurement for speaker verification using support vector machines (SVMs). An efficient Gaussian mixture models (GMMs) based quality estimation algorithm is proposed to potentially utilize speaker-specific broad acoustic-class characteristics. Some verification strategies are also considered in the test phase. We perform clustering-based vector p...

Fast speaker verification systems can be realised by reducing the computation associated with searching of mixture components within the statistical model such as a Gaussian mixture model, GMM. Several improvements regarding computational efficiency have already been proposed for speaker verification. In this paper, the technique of Gaussian selection is applied to the speaker verification task...

In this paper we describe the major elements of MIT Lincoln Laboratory’s Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker...

Performing speaker verification in the simultaneous presence of ageing progression and changing speech sample quality is an important, open problem. The issues of ageing and quality variation go hand in hand; the effect of ageing increases with time, while variations in quality are also more likely to be encountered as time passes. In this work we demonstrate the effect of ageing on speaker ver...

The aim of this report was to implement a text-dependent speaker verification system using speaker adapted neural networks and to evaluate the system. The idea was to use a hybrid HMM/ANN approach, i.e. Artificial Neural Networks were used to estimate Hidden Markov Model emission posterior probabilities from speech data, and the system was implemented in C++ as a module for GIVES. The report al...

Spoken language is used by human to convey many types of information. Primarily, speech convey message via words. Owing to advanced speech technologies, people's interactions with remote machines, such as phone banking, internet browsing, and secured information retrieval by voice, is becoming popular today. Speaker verification and speaker identification are important for authentication and ve...

Besides background noise, channel effect and speaker’s health condition, emotion is another factor which may influence the performance of a speaker verification system. In this paper, the performance of a GMM-UBM based speaker verification system on emotional speech is studied. It is found that speech with various emotions aggravates the verification performance. Two reasons for the performance...