  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.
