Unsupervised Speaker Change Detection Using SVM Training Misclassification Rate

Authors:
Po-Chuan Lin;Jia-Ching Wang;Jhing-Fa Wang;Hao-Ching Sung
Affiliations:
-;-;-;-
Venue:
IEEE Transactions on Computers
Year:
2007

Citing 0
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BIC-based speaker segmentation using divide-and-conquer strategies with application to speaker diarization

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Abstract

This work presents an unsupervised speaker change detection algorithm based on support vector machine (SVM) to detect speaker change in a speech stream. The proposed algorithm is called the SVM training misclassification rate (STMR). The STMR can identify speaker changes 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%.