一种新的用于说话人辨认的PNN分类器的研究

给出了一种新的类条件密度函数估计的σPNN模型,它基于模式层共享的PNN和模式 层分离的PNN,即每个类不仅拥有一组只属于自己的模式层,还拥有所有类都共享的几个模式 层,这里共享意味着每个核函数对所有类的条件密度估计都有贡献,新模型的训练采用最大似然 准则,并改进了EM算法来调整模型参数.闭集文本自由说话人辨认试验证明了提出的模型及其 算法的正确性．

A Novel PNN Classification for Speaker Identification

A novel a PNN model is proposed for class conditional density estimation based on the mixtures of PNN of shared pattern layers and PNN of separated pattern layers. Each class not only has a set of pattern layers belonging to itself, but also has several pattern layers shared for all class, where "shared" means that each kernel may contribute to the estimation of the conditional density of all classes. The training of the novel model utilizes the maximum likelihood criterion and an effective EM algorithms to adjust model parameters .s developed. These results of the closed-set text-independent speaker identification experiments indicate the proposed model and algorithms improve identification accuracy.