Unsupervised speaker clustering using SVM training missclassification rate for meeting short-duration speech signals

Po Chuan Lin, Yeh Yi Jui, Tsai Sung Ying, Yeong Chin Chen, Menq-Jiun Wu

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

Abstract

This paper proposes an unsupervised speaker clustering system for duration of speech signals below 4 seconds. For determining whether two collected speech sections uttered from the same speaker or not, our previous SVM training miss-classification rate (STMR) is adopted to evaluate the data separability between two different speakers. This paper also proposes a hierarchical extract and merge (HEM) clustering method to reduce agglomeration time and enhance the clustering purity. Experiment results show the average speaker purity (ASP) and average cluster purity (ACP) are both better than the CE manner with the GMM training miss-classification rates (GTMR) for 2 to 4 seconds short speech sections.

Original languageEnglish
Title of host publicationProceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010
Pages606-609
Number of pages4
DOIs
Publication statusPublished - 2010 Dec 1
Event4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010 - Shenzhen, China
Duration: 2010 Dec 132010 Dec 15

Other

Other4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010
CountryChina
CityShenzhen
Period10-12-1310-12-15

Fingerprint

Speech Signal
Clustering
Agglomeration
Separability
Clustering Methods
Evaluate
Experiment
Training
Speech
Experiments

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Lin, P. C., Jui, Y. Y., Ying, T. S., Chen, Y. C., & Wu, M-J. (2010). Unsupervised speaker clustering using SVM training missclassification rate for meeting short-duration speech signals. In Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010 (pp. 606-609). [5715505] https://doi.org/10.1109/ICGEC.2010.155
Lin, Po Chuan ; Jui, Yeh Yi ; Ying, Tsai Sung ; Chen, Yeong Chin ; Wu, Menq-Jiun. / Unsupervised speaker clustering using SVM training missclassification rate for meeting short-duration speech signals. Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010. 2010. pp. 606-609
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abstract = "This paper proposes an unsupervised speaker clustering system for duration of speech signals below 4 seconds. For determining whether two collected speech sections uttered from the same speaker or not, our previous SVM training miss-classification rate (STMR) is adopted to evaluate the data separability between two different speakers. This paper also proposes a hierarchical extract and merge (HEM) clustering method to reduce agglomeration time and enhance the clustering purity. Experiment results show the average speaker purity (ASP) and average cluster purity (ACP) are both better than the CE manner with the GMM training miss-classification rates (GTMR) for 2 to 4 seconds short speech sections.",
author = "Lin, {Po Chuan} and Jui, {Yeh Yi} and Ying, {Tsai Sung} and Chen, {Yeong Chin} and Menq-Jiun Wu",
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Lin, PC, Jui, YY, Ying, TS, Chen, YC & Wu, M-J 2010, Unsupervised speaker clustering using SVM training missclassification rate for meeting short-duration speech signals. in Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010., 5715505, pp. 606-609, 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010, Shenzhen, China, 10-12-13. https://doi.org/10.1109/ICGEC.2010.155

Unsupervised speaker clustering using SVM training missclassification rate for meeting short-duration speech signals. / Lin, Po Chuan; Jui, Yeh Yi; Ying, Tsai Sung; Chen, Yeong Chin; Wu, Menq-Jiun.

Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010. 2010. p. 606-609 5715505.

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

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N2 - This paper proposes an unsupervised speaker clustering system for duration of speech signals below 4 seconds. For determining whether two collected speech sections uttered from the same speaker or not, our previous SVM training miss-classification rate (STMR) is adopted to evaluate the data separability between two different speakers. This paper also proposes a hierarchical extract and merge (HEM) clustering method to reduce agglomeration time and enhance the clustering purity. Experiment results show the average speaker purity (ASP) and average cluster purity (ACP) are both better than the CE manner with the GMM training miss-classification rates (GTMR) for 2 to 4 seconds short speech sections.

AB - This paper proposes an unsupervised speaker clustering system for duration of speech signals below 4 seconds. For determining whether two collected speech sections uttered from the same speaker or not, our previous SVM training miss-classification rate (STMR) is adopted to evaluate the data separability between two different speakers. This paper also proposes a hierarchical extract and merge (HEM) clustering method to reduce agglomeration time and enhance the clustering purity. Experiment results show the average speaker purity (ASP) and average cluster purity (ACP) are both better than the CE manner with the GMM training miss-classification rates (GTMR) for 2 to 4 seconds short speech sections.

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Lin PC, Jui YY, Ying TS, Chen YC, Wu M-J. Unsupervised speaker clustering using SVM training missclassification rate for meeting short-duration speech signals. In Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010. 2010. p. 606-609. 5715505 https://doi.org/10.1109/ICGEC.2010.155