Kawthar Yasmine Zergat

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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)
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)
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