Abstract — This paper addresses the problem of unsupervised speaker change detection. We assume that there is no prior knowledge on the number of speakers or their identities. Two methods are tested. The first method uses the Bayesian Information Criterion (BIC), investigates the AudioSpectrumCentroid and AudioWaveformEnvelope features, and implements a dynamic thresholding followed by a fusion scheme. The second method is a real-time one that uses a metric-based approach employing line spectral pairs (LSP) and the BIC criterion to validate a potential change point. The experiments are carried out on two different datasets. The first set was created by concatenating speakers from the TIMIT database and is referred to as the TIMIT data set. The second set was created by using recordings from the MPEG-7 test set CD1 and broadcast news and is referred to as the INESC dataset. I
