Speaker dependency of spectral features and speech production cues for automatic emotion classification

Spectral and excitation features, commonly used in automatic emotion classification systems, parameterise different aspects of the speech signal. This paper groups these features as speech production cues, broad spectral measures and detailed spectral measures and looks at how they differ in their performance in both speaker dependent and speaker independent systems. The extent of speaker normalisation on these features is also considered. Combinations of different features are then compared in terms of classification accuracies. Evaluations were conducted on the LDC emotional speech corpus for a five-class problem. Results indicate that MFCCs are very discriminative but suffer from speaker variability. Further, results suggest that the best front end for a speaker independent system is a combination of pitch, energy and formant information.