Abstract: The effective handling of overlapping speech is at the limits of the current state-of-the-art in speaker diarization. This paper presents our latest work in overlap detection. We report the combination of features derived through convolutive nonnegative sparse coding and new energy, spectral and voicingrelated features within a conventional HMM system. Overlap detection results are fully integrated into our top-down diarization system through the application of overlap exclusion and overlap labeling. Experiments on a subset of the AMI corpus show that the new system delivers significant reductions in missed speech and speaker error. Through overlap exclusion and labelling the overall diarization error rate is shown to improve by 6.4 % relative.

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