Blind Segmentation and Labeling of Speakers via the Bayesian Information Criterion for Video-Conference Indexing

Abstract:  The purpose of this paper is to present a system which breaks input speech into segments and identifies each new appearance of the same speaker with a consistent label. This task adds up to a topic detection system that makes use of key-word recognition to obtain suitable labels for an automatic indexing system project. Both the segments definition and the identification of the speaker for each segment are performed using an acoustic similarity measure. Our task is to separate and identify the different speakers who appear in a video-conference session without any prior knowledge of the speakers or their number. The first aim is to detect the time points where a speaker change takes place using a robust acoustic change detection (ACD) system. Afterwards, the regions defined by these time marks are labeled with the use of a clustering algorithm. The Bayesian Information Criterion (BIC) is the key element in the system, and is used in several ways as a measure to compare speech. EERs of 13.66 % are obtained for this task with a soft feeding back of clustering information to enhance ACD performance.