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A hybrid gmm/svm system for text independent speaker identification.
| Content Provider | CiteSeerX |
|---|---|
| Author | Djemili, Rafik Bedda, Mouldi Bourouba, Hocine |
| Abstract | 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. Keywords—Speaker identification, Gaussian mixture model (GMM), support vector machine (SVM), hybrid GMM/SVM. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Support Vector Machine Text Independent Speaker Identification Hybrid Gmm Svm System Gaussian Mixture Model Keywords Speaker Identification Speech Token Discriminant Model Paradigm Hybrid Gmm Svm Text Independent Speaker Identification Task Hybrid Scheme Hierarchical Tree Structure Baseline Statistical Model Statistical Model Novel Approach Whole Speaker Space Experimental Result Identification Error Rate Small Subset |
| Content Type | Text |
