Emotion Recognition based on Third-Order Circular Suprasegmental Hidden Markov Model
It addresses emotion recognition for speech processing applications, but appears incremental as it builds on existing HMM methods with specific modifications.
This paper tackles emotion recognition from speech by proposing a Third-Order Circular Suprasegmental Hidden Markov Model (CSPHMM3) as a classifier, achieving an average accuracy of 77.8% on the EPST database and outperforming several baseline models by 3.5% to 6.0%.
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.