This paper addresses the problem of unsupervised speaker change detection. Three systems based on the Bayesian information criterion (BIC) are tested. The first system investigates the AudioSpectrumCentroid and the AudioWaveformEnvelope features, implements a dynamic thresholding followed by a fusion scheme, and finally applies BIC. The second method is a real-time one that uses a metric-based approach employing the line spectral pairs and the BIC to validate a potential speaker change point. The third method consists of three modules. In the first module, a measure based on second-order statistics is used; in the second module, the Euclidean distance and T 2  hotelling statistic are applied; and in the third module, the BIC is utilized. The experiments are carried out on a dataset created by concatenating speakers from the TIMIT database, that is referred to as the TIMIT data set. A comparison between the performance of the three systems is made based on t-statistics
['Margarita Kotti', 'Luis Gustavo P. M. Martins', 'Emmanouil Benetos', 'Jaime S. Cardoso', 'Constantine Kotropoulos']
Automatic Speaker Segmentation using Multiple Features and Distance Measures: A Comparison of Three Approaches