Kawthar Yasmine Zergat

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Most of the existing speaker recognition systems are based on the basic GMM, the state of the art GMM-UBM, the SVM or more recently the GMM-SVM modeling. In this paper, a new scheme for Automatic Speaker Recognition (ASR), namely GMM-PCA-SVM, is presented. Dimensionality reduction using Principal Component Analysis (PCA) technique, which was previously(More)
An important step in speaker verification is extracting features that best characterize the speaker voice. This paper investigates a front-end processing that aims at improving the performance of speaker verification based on the SVMs classifier, in text independent mode. This approach combines features based on conventional Mel-cepstral Coefficients(More)
This paper presents an automatic speaker verification system based on the hybrid GMM-SVM model working in real environment. An important step in speaker verification is extracting features that best characterized the speaker. Mel-Frequency Cepstral Coefficients (MFCC) and their firt and second derivatives are commonly used as acoustic features for speaker(More)
This paper deals with an Arabic text-independent speaker verification system over the Internet Protocol (VoIP). The system, using the ARADIGIT database and based on Support Vector Machine (SVM), was designed to use the information extracted directly from the coded parameters embedded in the ITU-T G.729 bitstream. Experiments evaluated the robustness of the(More)
This paper deals with text-independent speaker verification system based on spoken Arabic digits in real environment. In this work, we adopted Mel-Frequency Cepstral Coefficients (MFCC) as the speaker speech feature parameters, Gaussian Mixture Model (GMM) are used for modeling the extracted speech feature and training the support vector machines (SVMs).(More)
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