Abstract: Feature selection and extraction play a vital role in speech emotion recognition.At present,no effective speech emotion features are proposed.For these reasons,according to the characteristic of Mandarin which is different from western languages,some effective emotional features and related statistics,including Mel-frequency cepstral coefficients,pitch frequency,short-time energy,short-time average zerocrossing rate,the first formant and so on,were analyzed on a Mandarin emotional corpus which contains 6 kinds of emotions.Then we chose principal component analysis (PCA) for extraction and presented a speech emotion recognition method based on support vector machine (SVM) for classification.The experimental results show that the proposed method achieves high emotion recognition accuracy compared with several significant methods,and the emotion extraction and modeling are reasonable and effective.

Key words: Speech emotion recognition,PCA,SVM