Improving speaker segmentation via speaker identification and text segmentation

Speaker segmentation is an essential part of a speaker diarization system. Common segmentation systems usually miss speaker change points when speakers switch fast. These errors seriously confuse the following speaker clustering step and result in high overall speaker diarization error rates. In this paper two methods are proposed to deal with this problem: The first approach uses speaker identification techniques to boost speaker segmentation. And the second approach applies text segmentation methods to improve the performance of speaker segmentation. Experiments on Quaero speaker diarization evaluation data shows that our methods achieve up to 45% relative reduction in the speaker diarization error and 64% relative increase in the speaker change detection recall rate over the baseline system. Moreover, both these two approaches can be considered as post-processing steps over the baseline segmentation, therefore, they can be applied in any speaker diarization systems.