Detecting Emotions in Mandarin Speech

In this paper, a Mandarin speech based emotion classification method is presented. Five primary human emotions including anger, boredom, happiness, neutral and sadness are investigated. For speech emotion recognition, we select 16 LPC coefficients, 12 LPCC components, 16 LFPC components, 16 PLP coefficients, 20 MFCC components and jitter as the basic features to form the feature vector. Two text-dependent and speaker-independent corpora are employed. The recognizer presented in this paper is based on three recognition techniques: LDA, K-NN, and HMMs. Results show that the selected features are robust and effective in the emotion recognition at the valence degree in both corpora. For the LDA emotion recognition, the highest accuracy of 79.9% is obtained. For the K-NN emotion recognition, the highest accuracy of 84.2% is obtained. And for the HMMs emotion recognition, the highest accuracy of 88.7% is achieved.