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A Study on The Improvement of Emotion Recognition by Gender Discrimination  

Cho, Youn-Ho (Dept. of Computer Science & Engineering, Dankook University)
Park, Kyu-Sik (Dept. of Computer Science & Engineering, Dankook University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.45, no.4, 2008 , pp. 107-114 More about this Journal
Abstract
In this paper, we constructed a speech emotion recognition system that classifies four emotions - neutral, happy, sad, and anger from speech based on male/female gender discrimination. At first, the proposed system distinguish between male and female from a queried speech, then the system performance can be improved by using separate optimized feature vectors for each gender for the emotion classification. As a emotion feature vector, this paper adopts ZCPA(Zero Crossings with Peak Amplitudes) which is well known for its noise-robustic characteristic from the speech recognition area and the features are optimized using SFS method. For a pattern classification of emotion, k-NN and SVM classifiers are compared experimentally. From the computer simulation results, the proposed system was proven to be highly efficient for speech emotion classification about 85.3% regarding four emotion states. This might promise the use the proposed system in various applications such as call-center, humanoid robots, ubiquitous, and etc.
Keywords
ZCPA; SVM; SFS;
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