Speech Emotion Recognition Using Spectral Entropy

Authors:
Woo-Seok Lee;Yong-Wan Roh;Dong-Ju Kim;Jung-Hyun Kim;Kwang-Seok Hong
Affiliations:
School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea 440-746;School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea 440-746;School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea 440-746;School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea 440-746;School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea 440-746
Venue:
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part II
Year:
2008

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Abstract

This paper proposes a Gaussian Mixture Model (GMM)---based speech emotion recognition methods using four feature parameters; 1) Fast Fourier Transform(FFT) spectral entropy, 2) delta FFT spectral entropy, 3) Mel-frequency Filter Bank (MFB) spectral entropy, 4) delta MFB spectral entropy. In addition, we use four emotions in a speech database including anger, sadness, happiness, and neutrality. We perform speech emotion recognition experiments using each pre-defined emotion and gender. The experimental results show that the proposed emotion recognition using FFT spectral-based entropy and MFB spectral-based entropy performs better than existing emotion recognition based on GMM using energy, Zero Crossing Rate (ZCR), Linear Prediction Coefficient (LPC), and pitch parameters.