Unsupervised Speaker Change Detection for Broadcast News Segmentation

Kasper Winther Jørgensen, Lasse Lohilahti Mølgaard, Lars Kai Hansen

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    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.
    Original languageEnglish
    Title of host publicationEusipco
    Publication date2006
    Publication statusPublished - 2006
    Event14th European Signal Processing Conference - Florence, Italy
    Duration: 4 Sep 20068 Sep 2006


    Conference14th European Signal Processing Conference

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