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Overlapping speech is known to degrade speaker diarization performance with impacts on speaker clustering and segmentation. While previous work made important advances in detecting overlapping speech intervals and in attributing them to relevant speakers, the problem remains largely unsolved. This paper reports the first application of convolutive non-negative sparse coding (CNSC) to the overlap problem...
This paper presents a new multimodal approach to speaker diarization of TV show data. We hypothesize that the intraspeaker variation in visual information might be less than that in the corresponding acoustic information and therefore might be better suited to the task of speaker model initialisation. This is an acknowledged weakness of the computationally efficient top-down approach to speaker diarization...
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