Relative entropy normalized Gaussian supervector for speech emotion recognition using kernel extreme learning machine

2016 ◽  
Author(s):  
Ruru Li ◽  
Dali Yang ◽  
Xinxing Li ◽  
Renyu Wang ◽  
Mingxing Xu ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Extreme Learning Machine ◽  
Relative Entropy ◽  
Speech Emotion Recognition ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Speech emotion recognition based on feature selection and extreme learning machine decision tree

2018 ◽  
Vol 273 ◽  
pp. 271-280 ◽  
Author(s):  
Zhen-Tao Liu ◽  
Min Wu ◽  
Wei-Hua Cao ◽  
Jun-Wei Mao ◽  
Jian-Ping Xu ◽  
...  
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
Emotion Recognition ◽  
Extreme Learning Machine ◽  
Speech Emotion Recognition ◽  
Learning Machine

scholarly journals Connecting Subspace Learning and Extreme Learning Machine in Speech Emotion Recognition

2019 ◽  
Vol 21 (3) ◽  
pp. 795-808 ◽  
Author(s):  
Xinzhou Xu ◽  
Jun Deng ◽  
Eduardo Coutinho ◽  
Chen Wu ◽  
Li Zhao ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Extreme Learning Machine ◽  
Subspace Learning ◽  
Speech Emotion Recognition ◽  
Learning Machine

scholarly journals Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Hariharan Muthusamy ◽  
Kemal Polat ◽  
Sazali Yaacob
Keyword(s):  
Emotion Recognition ◽  
Extreme Learning Machine ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
Gaussian Mixture ◽  
Speech Emotion Recognition ◽  
New Feature ◽  
Learning Machine

Recently, researchers have paid escalating attention to studying the emotional state of an individual from his/her speech signals as the speech signal is the fastest and the most natural method of communication between individuals. In this work, new feature enhancement using Gaussian mixture model (GMM) was proposed to enhance the discriminatory power of the features extracted from speech and glottal signals. Three different emotional speech databases were utilized to gauge the proposed methods. Extreme learning machine (ELM) andk-nearest neighbor (kNN) classifier were employed to classify the different types of emotions. Several experiments were conducted and results show that the proposed methods significantly improved the speech emotion recognition performance compared to research works published in the literature.

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

2020 ◽  
Vol 13 ◽  
Author(s):  
Renxiong Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Prediction Methods ◽  
Multiple Kernel ◽  
Kernel Extreme Learning Machine ◽  
Useful Life ◽  
Learning Machine

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results : Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

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