Abstract：To solve the problems of sparse representation recognition algorithm with a large number of samples to train overcomplete dictionary and high degree of feature redundancy, the recognition algorithm of speech emotion on small samples by overcomplete dictionary learning and PCA dimension reduction was proposed. The feature dimensions were reduced by PCA method, and the over-complete dictionary was trained to identify samples. The speech emotional feature sparse representation was given to determine the steps of the proposed algorithm. The proposed algorithm was compared with BP and SVM for the same feature dimensions. The effects of the feature sparse representing on speech emotion recognition rate, time efficiency and space efficiency were analyzed. The experimental results show that the recognition rate of the proposed algorithm is better than those of SVM and BP, and the sparse feature is easy to handle. Using sparse features, the average recognition rate is increased by nearly 15%， and the time efficiency is improved by nearly 50% with increased space efficiency of 33%.
毛启容, 赵小蕾, 白李娟, 王治锋, 詹永照. 结合过完备字典与PCA的小样本语音情感识别方法[J]. 江苏大学学报（自然科学版）, 2013, 34(1): 60-65.
MAO Qi-Rong, ZHAO Xiao-Lei, BAI Li-Juan, WANG Zhi-Feng, ZHAN Yong-Zhao. Recognition of speech emotion on small samples by over-complete dictionary learning and PCA dimension reduction[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2013, 34(1): 60-65.
2013, Vol. 34 
