Emotion Recognition based on Third-Order Circular Suprasegmental Hidden Markov Model

Abstract

This work focuses on recognizing the unknown emotion based on the “Third-Order Circular Suprasegmental Hidden Markov Model (CSPHMM3)” as a classifier. Our work has been tested on “Emotional Prosody Speech and Transcripts (EPST)” database. The extracted features of EPST database are Mel-Frequency Cepstral Coefficients (MFCCs). Our results give average emotion recognition accuracy of 77.8% based on the CSPHMM3. The results of this work demonstrate that CSPHMM3 is superior to the Third-Order Hidden Markov Model (HMM3), Gaussian Mixture Model (GMM), Support Vector Machine (SVM), and Vector Quantization (VQ) by 6.0%, 4.9%, 3.5%, and 5.4%, respectively, for emotion recognition. The average emotion recognition accuracy achieved based on the CSPHMM3 is comparable to that found using “subjective assessment by human judges”.