Mandarin emotion recognition in speech

Abstract

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Humans interact with others in several ways, such as speech, gesture, eye contact etc. Among them, speech is the most effective way of communication through which people can readily exchange information without the need for any other tool. Emotions color the speech, and can make the meaning more complex and tell about how it is said. A Mandarin speech based emotion classification method is presented. Five basic human emotions, anger, boredom, happiness, neutral and sadness, are investigated. The features extracted include 16 LPC (linear predictive cepstrum) coefficients and 20 MFCC (Mel-frequency cepstral coefficients) components, and the presented recognizer is based on two statistical pattern recognition techniques, the minimum-distance method and the nearest class mean method. For minimum-distance emotion recognition, an average accuracy of 79.1% is obtained. For the nearest class mean emotion recognition, higher accuracy of 89.1% is achieved.