Unsupervised speaker change detection using SVM training misclassification rate

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

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish
Article number4288090
Pages (from-to)1234-1244
Number of pages11
JournalIEEE Transactions on Computers
Issue number9
StatePublished - Sep 2007


  • Speaker change detection
  • Speaker segmentation
  • Support vector machine


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