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.
Abstract (translated)
本文以三阶圆上节段隐马尔可夫模型（csphmm3）为分类器，对未知情绪进行识别。我们的工作已经在情绪韵律语言和转录（EPST）数据库上进行了测试。EPST数据库提取的特征是Mel频率倒谱系数（mfcs）。结果表明，基于CSPHMM3的情绪识别平均准确率为77.8%。研究结果表明，CSPHMM3在情感识别方面优于三阶隐马尔可夫模型（HMM3）、高斯混合模型（GMM）、支持向量机（SVM）和矢量量化（VQ），分别提高了6.0%、4.9%、3.5%和5.4%。基于csphmm3获得的平均情绪识别准确度与通过人类法官的主观评估得出的结果相当。
URL
https://arxiv.org/abs/1903.09803
