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

arXiv: https://arxiv.org/abs/1903.09803 · HTML: https://mlsanity.com/paper/1903.09803/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2019.

## Built on by (most cited first, 1 of 1)

- [Novel Dual-Channel Long Short-Term Memory Compressed Capsule Networks for Emotion Recognition](https://mlsanity.com/paper/2112.13350/) (2021)

## Abstract (arXiv)

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.