Improved AdaBoost Algorithm Using VQMAP for Speaker Identification
Haiyang Wu, Yong Jun Lu, Zhenyang Wu · 2010
Adaptive boosting (AdaBoost) learning method can improve the performance of a base classifier by mining feature information in depth. But it is computationally expensive, and the base classifier without a suitable accuracy will cause over fitting. In this paper an improved Adaboost algorithm using maximum a posteriori vector quantization model (VQMAP) for speaker identification is presented. A suitable VQMAP classifier matched the size of speaker identification problem is constructed first. Then it is boosted to a strong classifier by AdaBoost with early stopping method. Experiments show that the performance of the boosted VQMAP classifier is better than that of VQMAP, and is slightly lower than that of maximum a posteriori adapted Gaussian mixture model (GMMMAP), but with a faster recognition speed. In the case of limited data and predictable speaker number, it will reach or exceeded GMMMAP.