標題: Speaker-and-environment change detection in broadcast news using maximum divergence common component GMM
作者: Wang, Yih-Ru
交大名義發表
National Chiao Tung University
關鍵字: speaker-and-environment change detection;common component;Gaussian mixture model;maximum divergence measure
公開日期: 2006
摘要: 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.
URI: http://hdl.handle.net/11536/17090
ISBN: 978-3-540-49665-6
ISSN: 0302-9743
期刊: Chinese Spoken Language Processing, Proceedings
Volume: 4274
起始頁: 106
結束頁: 115
Appears in Collections:Conferences Paper