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.
Cite
@article{arxiv.1903.09803,
title = {Emotion Recognition based on Third-Order Circular Suprasegmental Hidden Markov Model},
author = {Ismail Shahin},
journal= {arXiv preprint arXiv:1903.09803},
year = {2019}
}
Comments
Accepted at The 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), Jordan