Investigating the Robustness of Teager Energy Cepstrum Coefficients for Emotion Recognition in Noisy Conditions

This paper investigated the robustness of Teager Energy Cepstrum Coefficient (TECC) in differentiating emotion categories for speech at different White Gaussian noise levels by comparing the performance with MFCC. Experiments involved the normalized squared error measurement, the multi-classes (four classes) emotion classification and the pair-wise emotion classification. This study included four emotion categories (neutral, happy, sad, and happy) from three databases (two English, one German). The result showed that TECC performed equally or outperformed MFCC in both multiemotion and pair-wise emotion classifications at all noise levels for all three databases. Using TECC features only, up to 89% for the four-emotion classification and 99% for the pair-wise emotion classification accuracy rate could be achieved.