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Content Provider | IEEE Xplore Digital Library |
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Author | Tsang-Long Pao Yu-Te Chen |
Copyright Year | 2003 |
Description | Author affiliation: Dept. of Comput. Sci. & Eng., Tatung Univ., Taiwan (Tsang-Long Pao; Yu-Te Chen) |
Abstract | 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. |
Starting Page | 227 |
Ending Page | 230 |
File Size | 275856 |
Page Count | 4 |
File Format | |
ISBN | 0780379802 |
DOI | 10.1109/ASRU.2003.1318445 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2003-11-30 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Emotion recognition Speech Humans Pattern recognition Feature extraction Data mining Linear predictive coding Cepstrum Mel frequency cepstral coefficient Cepstral analysis |
Content Type | Text |
Resource Type | Article |
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