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 ( $$F_0$$F0 ), the strength of excitation, the energy of excitation and the ratio of the high-frequency to low-frequency band energy ( $$\beta $$β ). 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.

Classification models based on multilayer perceptron (MLP) or support vector machine (SVM) have been commonly used for complex pattern classification tasks. These models are suitable for classification of fixed di- mension patterns. However, durations of consonant- vowel (CV) utterances vary not only for different classes, but also for a particular CV class. It is necessary to de- velop a method for representing the CV utterances by patterns of fixed dimension. For CV utterances, vowel onset point (VOP) is the instant at which the consonant part ends and the vowel part begins. Important infor- mation necessary for classification of CV utterances is present in the region around the VOP. A segment of fixed duration around the VOP can be processed to ex- tract a pattern of fixed dimension to represent a CV utterance. Accurate detection of vowel onset points is important for recognition of CV utterances of speech. In this paper, we propose an approach for detection of VOP, based on dynamic time alignment between a ref- erence pattern of a CV class and t he pattern of an utter- ance of that class. The results of studies show that the hypothesised VOPs using the proposed approach have less deviation from their actual locations.