A Two-level Method for Unsupervised Speaker-based Audio Segmentation

Authors:
Shilei Zhang;Shuwu Zhang;Bo Xu
Affiliations:
Chinese Academy of Sciences, Beijing, China;Chinese Academy of Sciences, Beijing, China;Chinese Academy of Sciences, Beijing, China
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Year:
2006

Citing 0
Cited 2

Phonemic segmentation using the generalised Gamma distribution and small sample Bayesian information criterion

Speech Communication
Style learning based story boundary detection for Chinese broadcast news videos

Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a two-level segmentation method that detects speaker changes in a continuous audio stream effectively. In our approach, we divide the change detection process into two levels: region level that detects the potential change regions containing candidate speaker change points, and boundary level that searches and refines the true change points. At the region level, we employ the modified Generalized Likelihood Ratio (MGLR) metric to search for the potential change regions in continuous local windows. At the boundary level, we perform T2 and Bayesian Information Criterion (BIC) algorithm to detect segment boundaries within the potential windows. The experimental results on the 1997 Broadcast News Hub4-NE mandarin corpus show the efficiency of the proposed scheme.