Unsupervised Speaker Change Detection for Broadcast News Segmentation

Kasper Winther Jørgensen, Lasse Lohilahti Mølgaard, Lars Kai Hansen

AbstractThis 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.
KeywordsChange detection, speech, segmentation
TypeConference paper [With referee]
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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