Fusion of Acoustic and Prosodic Features for Speaker Clustering

This work focus on a speaker clustering methods that are used in speaker diarization systems. The purpose of speaker clustering is to associate together segments that belong to the same speakers. It is usually applied in the last stage of the speaker-diarization process. We concentrate on developing of proper representations of speaker segments for clustering and explore different similarity measures for joining speaker segments together. We realize two different competitive systems. The first is a standard approach using a bottom-up agglomerative clustering principle with the Bayesian Information Criterion (BIC) as a merging criterion. In the next approach a fusion speaker clustering system is developed, where the speaker segments are modeled by acoustic and prosody representations. The idea here is to additionally model the speaker prosody characteristics and add it to basic acoustic information estimated from the speaker segments. We construct 10 basic prosody features derived from the energy of the audio signals, the estimated pitch contours, and the recognized voiced and unvoiced regions in speech. In this way we impose higher-level information in the representations of the speaker segments, which leads to improved clustering of the segments in the case of similar speaker acoustic characteristics or poor acoustic conditions.