An Adaptive BIC Approach for Robust Speaker Change Detection in Continuous Audio Streams

In this paper we focus on an audio segmentation. We present a novel method for robust and accurate detection of acoustic change points in continuous audio streams. The presented segmentation procedure was developed as a part of an audio diarization system for broadcast news audio indexing. In the presented approach, we tried to remove a need for using pre-determined decision-thresholds for detecting of segment boundaries, which are usually the case in the standard segmentation procedures. The proposed segmentation aims to estimate decision-thresholds directly from the currently processed audio data and thus reduces a need for additional threshold tuning from development data. It employs change-detection methods from two well-established audio segmentation approaches based on the Bayesian Information Criterion. Combining methods from both approaches enabled us to adaptively tune boundary-detection thresholds from the underlying processing data. All three segmentation procedures are tested and compared on a broadcast news audio database, where our proposed audio segmentation procedure shows its potential.