A two pass algorithm for speaker change detection

Speaker change detection is a necessary first step in several applications. In this paper, we propose an unsupervised two pass algorithm for speaker change detection in conversational speech. Generalized Likelihood Ratio (GLR) metric is used in the first pass to coarsely identify speaker change points and during the second pass, these candidate change points are finely analyzed assuming that the initial part of the conversational audio is from one of the speakers. The final change point detection decision is based on the likelihood probability function computed for the segments between two consecutive candidate change points using the known speaker model. The proposed two pass algorithm has been tested on a question and answer session of a financial audio report of a company and also on an audio track of a movie.