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Paper Count: 30054
A Hybrid GMM/SVM System for Text Independent Speaker Identification
Abstract:This paper proposes a novel approach that combines statistical models and support vector machines. A hybrid scheme which appropriately incorporates the advantages of both the generative and discriminant model paradigms is described and evaluated. Support vector machines (SVMs) are trained to divide the whole speakers' space into small subsets of speakers within a hierarchical tree structure. During testing a speech token is assigned to its corresponding group and evaluation using gaussian mixture models (GMMs) is then processed. Experimental results show that the proposed method can significantly improve the performance of text independent speaker identification task. We report improvements of up to 50% reduction in identification error rate compared to the baseline statistical model.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1079432Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
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