Excitation Features of Speech for Emotion Recognition Using Neutral Speech as Reference

In generation of emotional speech, there are deviations in the speech production features when compared to neutral (non-emotional) speech. The objective of this study is to capture the deviations in features related to the excitation component of speech and to develop a system for automatic recognition of emotions based on these deviations. The emotions considered in this study are anger, happiness, sadness and neutral state. The study shows that there are useful features in the deviations of the excitation features, which can be exploited to develop an emotion recognition system. The excitation features used in this study are the instantaneous fundamental frequency ($$F_0$$), the strength of excitation, the energy of excitation and the ratio of the high-frequency to low-frequency band energy ($$\beta $$). A hierarchical binary decision tree approach is used to develop an emotion recognition system with neutral speech as reference. The recognition experiments showed that the excitation features are comparable or better than the existing prosody features and spectral features, such as mel-frequency cepstral coefficients, perceptual linear predictive coefficients and modulation spectral features.