文章基本信息

标题：The combination of Sparse Principle Component Analysis and Kernel Ridge Regression methods applied to speech recognition problem
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作者：Loc Hoang Tran ; Linh Hoang Tran
期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2018
卷号：10
期号：2
出版社：International Center for Scientific Research and Studies
摘要：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.
关键词：kernel ridge regression; HMM; speech recognition; MFCC; PCA; ; Sparse PCA