Unsupervised speaker change detection using SVM training misclassification rate

Po Chuan Lin, Jia Ching Wang, Jhing Fa Wang, Hao Ching Sung

研究成果: 雜誌貢獻期刊論文同行評審

12 引文 斯高帕斯(Scopus)

摘要

This work presents an unsupervised speaker change detection algorithm based on support vector machines (SVM) to detect speaker change (SC) in a speech stream. The proposed algorithm is called the SVM training misclassification rate (STMR). The STMR can identify SCs with less speech data collection, making it capable of detecting speaker segments with short duration. According to experiments on the NIST Rich Transcription 2005 Spring Evaluation (RT-05S) corpus, the STMR has a missed detection rate of only 19.67 percent.

原文???core.languages.en_GB???
文章編號4288090
頁(從 - 到)1234-1244
頁數11
期刊IEEE Transactions on Computers
56
發行號9
DOIs
出版狀態已出版 - 9月 2007

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