Imperial College London
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ProfessorBjoernSchuller

Faculty of Engineering, Department of Computing

Professor of Artificial Intelligence
 
 
 
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Contact

 

+44 (0)20 7594 8357bjoern.schuller Website

 
 
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Location

 

574Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Xu:2019:10.1109/TMM.2018.2865834,
author = {Xu, X and Deng, J and Coutinho, E and Wu, C and Zhao, L and Schuller, BW},
doi = {10.1109/TMM.2018.2865834},
journal = {IEEE Transactions on Multimedia},
title = {Connecting Subspace Learning and Extreme Learning Machine in Speech Emotion Recognition},
url = {http://dx.doi.org/10.1109/TMM.2018.2865834},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - IEEE Speech Emotion Recognition (SER) is a powerful tool for endowing computers with the capacity to process information about the affective states of users in human-machine interactions. Recent research has shown the effectiveness of graph embedding based subspace learning and extreme learning machine applied to SER, but there are still various drawbacks in these two techniques that limit their application. Regarding subspace learning, the change from linearity to nonlinearity is usually achieved through kernelisation, while extreme learning machines only take label information into consideration at the output layer. In order to overcome these drawbacks, this paper leverages extreme learning machine for dimensionality reduction and proposes a novel framework to combine spectral regression based subspace learning and extreme learning machine. The proposed framework contains three stages - data mapping, graph decomposition, and regression. At the data mapping stage, various mapping strategies provide different views of the samples. At the graph decomposition stage, specifically designed embedding graphs provide a possibility to better represent the structure of data, through generating virtual coordinates. Finally, at the regression stage, dimension-reduced mappings are achieved by connecting the virtual coordinates and data mapping. Using this framework, we propose several novel dimensionality reduction algorithms, apply them to SER tasks, and compare their performance to relevant state-of-the-art methods. Our results on several paralinguistic corpora show that our proposed techniques lead to significant improvements.
AU  - Xu,X
AU  - Deng,J
AU  - Coutinho,E
AU  - Wu,C
AU  - Zhao,L
AU  - Schuller,BW
DO  - 10.1109/TMM.2018.2865834
PY  - 2019///
SN  - 1520-9210
TI  - Connecting Subspace Learning and Extreme Learning Machine in Speech Emotion Recognition
T2  - IEEE Transactions on Multimedia
UR  - http://dx.doi.org/10.1109/TMM.2018.2865834
ER  -
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