Speaker-and-environment change detection in broadcast news using maximum divergence common component GMM

Yih-Ru Wang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationChinese Spoken Language Processing - 5th International Symposium, ISCSLP 2006, Proceedings
Pages106-115
Number of pages10
DOIs
StatePublished - 1 Dec 2006
Event5th International Symposium on Chinese Spoken Language Processing, ISCSLP 2006 - Singapore, Singapore
Duration: 13 Dec 200616 Dec 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4274 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Symposium on Chinese Spoken Language Processing, ISCSLP 2006
CountrySingapore
CitySingapore
Period13/12/0616/12/06

Keywords

  • Common component Gaussian mixture model
  • Maximum divergence measure
  • Speaker-and-environment change detection

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