Emotion recognition from speech signal

Abstract

Emotion recognition is a rapidly growing research domain in recent years. Unlike humans, machines lack the abilities to perceive and show emotions. But human-computer interaction can be improved by automated emotions recognition, thereby reducing the need of human intervention. In this paper, four basic emotions (Anger, Happy, Fear and Neutral) are analyzed from emotional speech signals. Signal processing methods are used for obtaining the production features from these signals. Source feature the instantaneous fundamental frequency (F0), system features the formants and dominant frequencies, zero-crossing rate (ZCR), and the combined features signal energy are used for the analyses. F0 is obtained using zero-frequency filtering (ZFF), and formants and dominant frequencies using LP spectrum. Short-time signal energy (STE) and ZCR are obtained in the voiced and unvoiced regions using a rectangular window of 200 samples. Two databases, German and Telugu Emotion Databases are used to cross-validate the results. Distinct differences are observed between high-arousal emotions (Anger and Happy) and Neutral emotion. Results indicate overlap between Anger and Happy emotions. But distinct differences are observed in the features for Happy/Anger and Fear, and between Happy and Anger emotions which is otherwise a challenging problem. The insights gained may be helpful in range of applications.

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