With the growing trend toward remote security verification procedures for telephone banking, biometric security measures and similar applications, automatic speaker veri-fication (ASV) has received a lot of attention in recent years. The complexity of ASV system and its verification time depends on the number of feature vectors, their dimen-sionality, the complexity of the speaker models and the number of speakers. In this paper, we concentrate on optimizing dimensionality of feature space by selecting relevant fea-tures. At present there are several methods for feature selection in ASV systems. To improve performance of ASV system we present another method that is based on ant colony optimization (ACO) algorithm. After feature selection phase, feature vectors are applied to a Gaussian mixture model universal background model (GMM-UBM) which is a text-independent speaker verification model. The performance of proposed algorithm is compared to the performance of genetic algorithm on the task of feature selection in TIMIT corpora. The results of experiments indicate that with the optimized feature set, the performance of the ASV system is improved. Moreover, the speed of verification is significantly increased since by use of ACO, number of features is reduced over 80% which consequently decrease the complexity of our ASV system. 1

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 also contains an overview over speaker verification. Methods and algorithms for network training and adaptation are explained, and the performance of the system is tested. Both Multi-Layer perceptrons and Single-Layer perceptrons are tested and compared to other speaker verification systems. The test results show that the hybrid HMM/ANN system does not perform as well as other speaker verification systems, but if the system parameters are optimised further performance might increase. Along with an analysis and summary of the project possible improvements of the system are suggested. Sammanfattning Målet med denna rapport var att implementera ett textberoende talarverifieringssystem med

This paper provides an overview of Gaussian Mixture Model (GMM) and its component of speech signal. During the earlier period it has been revealed that Gaussian Mixture Model is very much appropriate for voice modeling in speaker recognition system. For Speaker recognition, Gaussian mixture model is an essential appliance of statistical clustering. The task effortlessly performed by humans is not effortless for machine or computers such as voice recognition or face recognition so for this function speaker recognition technology makes available a solution, using this technology the computers/machines outperforms than humans.