System Implementation of Emotion Recognition From Speech

Emotional states can be expressed in facial expressions, speech, or body language. This study aims to create an effective system that can recognize emotional states from speech. The study proposes a methodology framework that uses acoustic features to recognize emotions. Initially, various methods for extracting features from speech are examined, and extensive statistical values are derived from the feature data. To recognize emotions from different speakers, a method to standardize the statistical features is presented. Normalization is proven to be necessary for building a high-performance system. Using normalized values, a score is calculated for each speech utterance, and the feature patterns for different emotions such as Anger, Boredom, Fear, Sadness, Happiness, and Neutral are examined. The study suggests that pitch, first formant, and speaking rate are the best features to distinguish emotions such as Anger, Fear, Boredom, Happiness, and Sadness. The study also shows that Support Vector Machine (SVM) can yield satisfactory performance in recognizing emotions. The system has achieved a high level of accuracy in detecting emotions such as Fear, Boredom, and Sadness. However, the method used in this study is not effective in recognizing emotions of Anger and Happiness.