Publication: Universum autoencoder-based domain adaptation for speech emotion recognition
Universum autoencoder-based domain adaptation for speech emotion recognition
Date
Date
Date
Citations
Deng, J., Xu, X., Zhang, Z., Frühholz, S., & Schuller, B. (2017). Universum autoencoder-based domain adaptation for speech emotion recognition. IEEE Signal Processing Letters, 24(4), 500–504. https://doi.org/10.1109/LSP.2017.2672753
Abstract
Abstract
Abstract
One of the serious obstacles to the applications of speech emotion recognition systems in real-life settings is the lack of generalization of the emotion classifiers. Many recognition systems often present a dramatic drop in performance when tested on speech data obtained from different speakers, acoustic environments, linguistic content, and domain conditions. In this letter, we propose a novel unsupervised domain adaptation model, called Universum autoencoders, to improve the performance of the systems evaluated in mismatched traini
Additional indexing
Creators (Authors)
- Deng, Jun
- Xu, Xinzhou
- Zhang, Zixing
- Frühholz, Sascha
- Schuller, Bjorn
Volume
Volume
Volume
Number
Number
Number
Page Range
Page Range
Page Range
Page end
Page end
Page end
Item Type
Item Type
Item Type
In collections
Language
Language
Language
Publication date
Publication date
Publication date
Date available
Date available
Date available
ISSN or e-ISSN
ISSN or e-ISSN
ISSN or e-ISSN
OA Status
OA Status
OA Status
Publisher DOI
Citations
Deng, J., Xu, X., Zhang, Z., Frühholz, S., & Schuller, B. (2017). Universum autoencoder-based domain adaptation for speech emotion recognition. IEEE Signal Processing Letters, 24(4), 500–504. https://doi.org/10.1109/LSP.2017.2672753
