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Speaker-and-Environment Change Detection in Broadcast News Using Maximum Divergence Common Component GMM

Speaker-and-Environment Change Detection in Broadcast News Using Maximum Divergence Common Component GMM,10.1007/11939993_15,Yih-ru Wang

Speaker-and-Environment Change Detection in Broadcast News Using Maximum Divergence Common Component GMM  
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In this paper, the supervised maximum-divergence common component GMM (MD-CCGMM) model was used to the speaker-and-environment change detection in broadcast news signal. In order to discriminate the speaker-and-environment change in broadcast news, the MD-CCGMM signal model will maximize the likelihood of CCGMM signal modeling and the divergence measure of different audio signal segments simultaneously. Performance of the MD-CCGMM model was examined using a four-hour TV broadcast news database. A result of 16.0% Equal Error Rate (EER) was achieved by using the divergence measure of CCGMM model. When using supervised MD-CCGMM model, 14.6% Equal Error Rate can be achieved.
Conference: Chinese Spoken Language Processing - ISCSLP , pp. 106-115, 2006
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