A multimodal approach to initialisation for top-down speaker diarization of television shows

Abstract

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 that is used here. Experimental results show that a recently proposed approach to purification and the new multimodal approach to initialisation together deliver 22% and 17% relative improvements in diarization performance over the baseline system on independent development and evaluation datasets respectively.