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Recognition of speech emotion on small samples by over-complete dictionary learning and PCA dimension reduction |
School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China |
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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%.
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Received: 14 December 2011
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