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Unsupervised speaker change detection using SVM training misclassification rate

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4 Author(s)
Po-Chuan Lin ; Nat. Cheng Kung Univ., Tainan ; Jia-Ching Wang ; Jhing-Fa Wang ; Hao-Ching Sung

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.

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Computers, IEEE Transactions on  (Volume:56 ,  Issue: 9 )