文章基本信息

标题：A Combination Approach of Gaussian Mixture Models and Support Vector Machines for Speaker Identifica
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作者：Rafik Djemili ; Hocine Bourouba ; Amara Korba 等
期刊名称：The International Arab Journal of Information Technology
印刷版ISSN：1683-3198
出版年度：2009
卷号：6
期号：5
出版社：Zarqa Private University
摘要：Gaussian mixture models are commonly used in speaker identification and verification systems. However, owing to their non discriminant nature, Gaussian mixture models still give greater identification errors in the evaluation process. Partitioning speakers database in clusters based on some proximity criteria where only a single cluster Gaussian mixture models is run in every test, have been suggested in literature generally to speed up the identification process for very large databases. In this paper, we propose a hierarchical clustering scheme using the discriminant power of support vector machines. Speakers are divided into small subsets and evaluation is then processed by GMMs. Experimental results show that the proposed method reduced significantly the error in overall speaker identification tests
关键词：Speaker identification; GMM; SVM.