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- Title
The combination of Sparse Principle Component Analysis and Kernel Ridge Regression methods applied to speech recognition problem.
- Authors
Loc Hoang Tran; Linh Hoang Tran
- Abstract
Speech recognition is the important problem in pattern recognition research field. In this paper, the combination of the Sparse Principle Component Analysis method and the kernel ridge regression method will be applied to the MFCC feature vectors of the speech dataset available from IC Design lab at Faculty of Electricals-Electronics Engineering, University of Technology, Ho Chi Minh City. Experiment results show that the combination of the Sparse Principle Component Analysis method and the kernel ridge regression method outperforms the current state of the art Hidden Markov Model method and the kernel ridge regression method alone in speech recognition problem in terms of sensitivity performance measure.
- Subjects
SPEECH perception; PRINCIPAL components analysis; RIDGE regression (Statistics); KERNEL functions; SUPPORT vector machines
- Publication
International Journal of Advances in Soft Computing & Its Applications, 2018, Vol 10, Issue 2, p120
- ISSN
2710-1274
- Publication type
Article