Two Step Speaker Segmentation Method Using Bayesian Information Criterion And Adapted Gaussian Mixtures Models

This paper addresses the topic of online unsupervised speaker segmentation in a complex audio environment as it is present in the Broadcast News databases. A new two stage speaker change detection algorithm is proposed, which combines the Bayesian Information Criterion with an ABLS-SCD statistical framework where adapted Gaussian mixture models are used to achieve higher accuracy. To enhance the performance of the proposed method a sub-window dependent threshold selection strategy for the ABLS-SCD is introduced. Also an additional window selection strategy for the proposed method is presented. Experimental design and test evaluation were carried out on the Slovenian BNSI Broadcast News database.