In this paper, we investigate the use of agglomerative Information Bottleneck (aIB) clustering for the speaker diarization task of meetings data. In contrary to the state-of-the-art diarization systems that models individual speakers with Gaussian Mixture Models, the proposed algorithm is completely non parametric . Both clustering and model selection issues of nonparametric models are addressed in this work. The proposed algorithm is evaluated on meeting data on the RT06 evaluation data set.

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... e system is able to achieve Diarization Error Rates comparable to state-of-the-art systems at a much lower computational complexity.