期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 21, 期 3, 页码 795-808出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2018.2865834
关键词
Speech emotion recognition; extreme learning machine; subspace learning; graph embedding; spectral regression
资金
- China Scholarship Council
- European Union [338164]
- European Union's Horizon 2020 Research and Innovation Programme [645378, 645094]
- Natural Science Foundation of China [61673108, 61231002, 11701290]
- Natural Science Foundation for Jiangsu Higher Education Institutions [16KJB510031, 17KJB110012]
- NUPTSF [NY217149, NY217150]
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 kernelization, whereas extreme learning machines only take label information into consideration at the output layer. In order to overcome these drawbacks, this paper leverages extreme learning machines for dimensionality reduction and proposes a novel framework to combine spectral regression-based subspace learning and extreme learning machines. 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.
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