Ensemble of Support Vector Machine for Text-Independent Speaker Recognition

Summary In this paper, the ensemble of support vector machines is applied to text-independent speaker recognition, and the bagging-like model and boosting-like model are proposed by adopted the ensemble idea. The purposes of adopting this idea are to deal with the large scale speech data and improve the performance of speaker recognition. The distance-based and probability-based scoring methods are used to score the new utterance. Compared with the conventional vector-based speaker models (Vector Quantization and Gaussian Mixture Model), our method is hyperplan-based. The experiments have been run on the YOHO database, and the results show that our models can get attractive performances.

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