Speaker recognition systems are often used in the field of security, and a common example of their use is client voice authentication for some secured applications. Another application of speaker recognition is segmentation into homogenous parts of speech where each segment corresponds to one speaker’s speech. This process can also be very useful for improving the accuracy of speech recognition systems. Speech can also be used in the field of audio indexing. Recognizing the identity of speakers in a multi-speaker audio stream can provide some usable knowledge about its content. Two types of speaker recognition system exist: the text-dependent and text-independent systems. The first are speaker recognition systems where the verification texts and those saved during the enrollment phase are the same. As in online video indexing, the sentences are a priori unknown; we will focus here on text-independent systems. This paper is organized as follows: first we present the GMM classifier, and then the principle of LLR (LikeLihood Ratio) detection used to decide on the score given by a tested utterance.