City Research Online

Abstract

Unsupervised speaker change detection is addressed in this paper. Three speaker segmentation systems are examined. The first system investigates the AudioSpectrumCentroid and the AudioWaveformEnvelope features, implements a dynamic fusion scheme, and applies the Bayesian Information Criterion (BIC). The second system consists of three modules. In the first module, a second-order statistic-measure is extracted; the Euclidean distance and the T2 Hotelling statistic are applied sequentially in the second module; and BIC is utilized in the third module. The third system, first uses a metric-based approach, in order to detect potential speaker change points, and then the BIC criterion is applied to validate the previously detected change points. Experiments are carried out on a dataset, which is created by concatenating speakers from the TIMIT database. A systematic performance comparison among the three systems is carried out by means of one-way ANOVA method and post hoc Tukey’s method.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	DOI:10.1109/MMSP.2007.4412819 © 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Publisher Keywords:	ANOVA method , AudioSpectrumCentroid , AudioWaveformEnvelope features , BIC-based speaker segmentation systems , Bayesian Information Criterion , Euclidean distance , Hotelling statistic , dynamic fusion scheme , second-order statistic-measure , unsupervised speaker change detection
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Departments:	School of Science & Technology > Computer Science

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