Rania Chakroun

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The Gaussian mixture models (GMM) represent an efficient model that was broadly used in most of speaker recognition applications. This study introduces a novel method for speaker verification task. We propose a reduced feature vector employing new information detected from the speaker's voice for performing text-independent speaker verification applications(More)
Gaussian mixture models (GMM) have become the standard method used for speaker recognition systems. A recent discovery is that combining GMM approach with another classifier is an effective method for speaker classification. We consider the GMM supervector in the context of support vector machines (SVM). We construct a support vector machine tested with two(More)
Over the past decade, the field of automatic speaker recognition has been the subject of extensive research looking for an efficient determination of a person's identity. Despite the essential role played by acoustic characteristics in order to discriminate between speakers. The research of discriminative information about a person remains a major(More)
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