Speech Emotion Recognition Using Segmental Level Prosodic Analysis

In this paper, prosodic analysis of speech segments is performed to recognise emotions. Speech signal is segmented into words and syllables. Energy and pitch parameters are extracted from utterances, words and syllables separately to develop emotion recognition models. Eight emotions (anger, disgust, fear, happy, neutral, sad, sarcastic and surprise) of simulated emotion speech corpus, IITKGP SESC [1] are used in this work for recognition of emotions. Word boundaries are manually marked for 15 utterances of IITKGP-SESC. Syllable boundaries are detected using vowel onset points (VOPs) as anchor locations. Recognition performance of emotions using segmental level prosodic features is not found to be appreciable, but by combining spectral features along with prosodic features, emotion recognition performance is considerably improved. Support vector machines (SVM) and Gaussian mixture models (GMM) are used to develop emotion models to analyse different speech segments for emotion recognition.