Conference paper
Unsupervised Speaker Change Detection for Broadcast News Segmentation
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.
Language: | English |
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Publisher: | IEEE |
Year: | 2006 |
Pages: | 1-5 |
Proceedings: | 14th European Signal Processing Conference |
ISSN: | 22195491 |
Types: | Conference paper |
ORCIDs: | Mølgaard, Lasse Lohilahti and Hansen, Lars Kai |
Abstracts Atmospheric modeling Measurement Robustness Speech Speech processing Training VQ distortion measure broadcast news segmentation cepstral analysis divergence shape distance false alarm rate first level alarms mel frequency cepstral coefficients speaker identity speaker recognition statistical analysis symmetric Kullback-Leibler distance unsupervised speaker change detection vector quantisation vector quantization