Multi-Layer Hybrid Fuzzy Classification Based on SVM and Improved PSO for Speech Emotion Recognition

Electronics

Wei Gu¹  Hua Dang²  Chengbo Xue²  Shihan Huang²  Rongkun Jiang²  Yue Hao² 

[1] School of Information and Electronics, Beijing Institute of Technology (BIT), Beijing 100081, China;School of Integrated Circuits and Electronics, Beijing Institute of Technology (BIT), Beijing 100081, China;

关键词: speech emotion recognition;    fuzzy c-means;    particle swarm optimization;    support vector machines;   

DOI  :  10.3390/electronics10232891

来源: DOAJ

【 摘 要 】

Speech Emotion Recognition (SER) plays a significant role in the field of Human–Computer Interaction (HCI) with a wide range of applications. However, there are still some issues in practical application. One of the issues is the difference between emotional expression amongst various individuals, and another is that some indistinguishable emotions may reduce the stability of the SER system. In this paper, we propose a multi-layer hybrid fuzzy support vector machine (MLHF-SVM) model, which includes three layers: feature extraction layer, pre-classification layer, and classification layer. The MLHF-SVM model solves the above-mentioned issues by fuzzy c-means (FCM) based on identification information of human and multi-layer SVM classifiers, respectively. In addition, to overcome the weakness that FCM tends to fall into local minima, an improved natural exponential inertia weight particle swarm optimization (IEPSO) algorithm is proposed and integrated with fuzzy c-means for optimization. Moreover, in the feature extraction layer, non-personalized features and personalized features are combined to improve accuracy. In order to verify the effectiveness of the proposed model, all emotions in three popular datasets are used for simulation. The results show that this model can effectively improve the success rate of classification and the maximum value of a single emotion recognition rate is 97.67% on the EmoDB dataset.

【 授权许可】

Unknown