Multi-stream speaker diarization systems for the meetings domain

Abstract

In the context of speech and speaker recognition systems, it is well known that the combination of different feature streams can improve significantly their performance. However, the application of multi-stream (MS) techniques to speaker diarization systems has not been extensively studied. In this paper, we address this issue: we formulate different MS techniques, such as feature combination, probability combination and selection, for their specific application to the segmentation and clustering modules of a speaker diarization system. We evaluate the different methods proposed for the meetings domain (RT04s database) and two different pairs of streams: first, MFCC and PLP and second, MFCC and prosodic features. For both types of multi-streams, results show that the MS probability combination approach applied to the segmentation stage clearly outperforms the single-stream, MS feature combination and MS selection systems. Index Terms: speaker diarization, multi-stream features, prosodic features.