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Statistical Speech Feature Selection for Emotion Recognition  

Kwon Oh-Wook (Chungbuk National University)
Chan Kwokleung (University of California)
Lee Te-Won (University of California)
Publication Information
The Journal of the Acoustical Society of Korea / v.24, no.4E, 2005 , pp. 144-151 More about this Journal
Abstract
We evaluate the performance of emotion recognition via speech signals when a plain speaker talks to an entertainment robot. For each frame of a speech utterance, we extract the frame-based features: pitch, energy, formant, band energies, mel frequency cepstral coefficients (MFCCs), and velocity/acceleration of pitch and MFCCs. For discriminative classifiers, a fixed-length utterance-based feature vector is computed from the statistics of the frame-based features. Using a speaker-independent database, we evaluate the performance of two promising classifiers: support vector machine (SVM) and hidden Markov model (HMM). For angry/bored/happy/neutral/sad emotion classification, the SVM and HMM classifiers yield $42.3\%\;and\;40.8\%$ accuracy, respectively. We show that the accuracy is significant compared to the performance by foreign human listeners.
Keywords
Emotion Recognition; Support Vector Machines; Hidden Markov Models;
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