A kind of novel fuzzy Kernel Fisher Discrimination(KFD) algorithm based on principal component space of Kernel Principle Component Analysis(KPCA) is proposed to improve speech emotion discrimination rate and real-time processing and is used in speech emotion discrimination.KPCA was firstly used to reduce dimensions of and remove noises for speech feature vector,kernel principal component space was obtained according to transformation matrix,then in this feature space,fuzzy C-mean cluster was utilized to compute the menbership of each speech feature vector,and furthermore,inter-calss divergence factor and in-calss divergence factor in LDAalgorithm were redefined to form fuzzy KFD calssifier to conduct speech emotion disctimition.Simulation experiment results show that the proposed method in this paper hais higher recognition rate and better anti-noise performance than traditional SVM and kerenl Fisher discriminatin algorithm and is a kind of novel effective method for speech emotion discrimination.
邢玉娟 李恒杰 胡建军 王万军.基于KPCA和模糊核Fisher判别的语音情感识别[J].重庆工商大学学报（自然科学版）,2013,30(1):62-68
XING Yu-juan, LI Heng-jie, HU Jian-jun, WANG Wan-jun. Speech Emotion Recognition Based on KPCA and Fuzzy Fisher Discrimination[J]. Journal of Chongqing Technology and Business University(Natural Science Edition）,2013,30(1):62-68