PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Unsupervised speaker change detection for broadcast news segmentation
Kasper W. Jørgensen, Lasse Mølgaard and Lars Kai Hansen
In: 14th European Signal Processing Conference, 4-8 Sep 2006, Florence, Italy.


This paper presents a speaker change detection system for news broadcast segmentation based on a vector quantization (VQ) approach. The system does not make any assumption about the number of speakers or speaker identity. The system uses mel frequency cepstral coefficients and change detection is done using the VQ distortion measure and is evaluated against two other statistics, namely the symmetric Kullback-Leibler (KL2) distance and the so-called ‘divergence shape distance’. First level alarms are further tested using the VQ distortion. We find that the false alarm rate can be reduced without significant losses in the detection of correct changes. We furthermore evaluate the generalizability of the approach by testing the complete system on an independent set of broadcasts, including a channel not present in the training set.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
ID Code:2645
Deposited By:Lasse Mølgaard
Deposited On:22 November 2006