The content-based indexing task considered in this paper consists in recognizing from their voice, speakers involved in a conversation. A new approach for speaker-based segmentation, which is the first necessary step for this indexing task, is described. Our study is done under the assumptions that no prior information on speakers is available, that the number of speakers is unknown and that people do not speak simultaneously. Audio data indexing is commonly divided in two parts : audio data is first segmented with respect to speakers utterances and then resulting segments associated with a given speaker are merged together. In this work, we focus on the first part and we propose a new segmentation method based on second order statistics. The practical significance of this study is illustrated by applying our new technique to real data to show its efficiency
