Abstarct: In the every audio signal, it becomes very important answer questions like: “what was said?”, but also “who said it?” as information varies depending on who utters the spoken words. Within the speech technologies, The broad topic of acoustic indexing studies the classification of sounds into different classes/sources. Algorithms used for acoustic indexing worry about the correct classification of the sounds, but not necessarily about the correct separation of them when more than one exist in the same audio segment. These purely classification techniques have sometimes been called audio clustering, which benefit from the broad topic of clustering, well studies in many areas. When multiple sounds appear in the same audio signal one must turn his attention to techniques called as audio diarization to process them.These can include particular speakers, music, background noise sources. When the possible classes correspond to the different speakers in a recording these techniques are called speaker diarization. Speaker diarization can be defined in terms of being a subtype of audio diarization, where the speech segments of the signal are broken into the different speakers They aim at answering the question “Who spoke when?” given an audio signal. Algorithms doing speaker diarization need to locate each speaker turn and assign them to the appropriate speaker cluster. The output of the system is a set of segments with a unique ID assigned to each person that intervenes in the recording. In this project using VAD’s G.729B in once step for seprate voice & unvoice. Then in this system using BIC algorithm for speech’segmentation by using MFCC’s feature, root-MFCC’s feature, TDC & root-TDC feature for second step ,and at last in the system using SVM for clustering.