DocumentCode
1889606
Title
Notice of Retraction
Speech recognition based on a compound kernel support vector machine
Author
Jing Bai ; Xue-ying Zhang ; Yue-ling Guo
Author_Institution
Coll. of Inf. Eng., Taiyuan Univ. of Technol., Taiyuan
fYear
2008
fDate
10-12 Nov. 2008
Firstpage
696
Lastpage
699
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
To improve the learning and generalization ability of the machine-learning model, a new compound kernel that may pay attention to the similar degree between sample space and feature space is proposed. In this paper, used the new compound kernel support vector machine to a speech recognition system for Chinese isolated words, non-specific person and middle glossary quantity, and compared the speech recognition results with the SVM using traditional kernels and with using RBF network. Experiments showed that the SVM performance with the new compound kernel is much better than with traditional kernels and has higher correct recognition rates than ones of using RBF network in different SNRs, and is of shorter training time.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
To improve the learning and generalization ability of the machine-learning model, a new compound kernel that may pay attention to the similar degree between sample space and feature space is proposed. In this paper, used the new compound kernel support vector machine to a speech recognition system for Chinese isolated words, non-specific person and middle glossary quantity, and compared the speech recognition results with the SVM using traditional kernels and with using RBF network. Experiments showed that the SVM performance with the new compound kernel is much better than with traditional kernels and has higher correct recognition rates than ones of using RBF network in different SNRs, and is of shorter training time.
Keywords
glossaries; learning (artificial intelligence); natural language processing; radial basis function networks; speech recognition; support vector machines; word processing; Chinese isolated words; RBF network; compound kernel support vector machine; glossary quantity; machine learning; speech recognition; Artificial neural networks; Hidden Markov models; Kernel; Machine learning; Risk management; Space technology; Speech recognition; Support vector machine classification; Support vector machines; Terminology; compound; feature extraction; kernel; kernel function; speech recognition; support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Communication Technology, 2008. ICCT 2008. 11th IEEE International Conference on
Conference_Location
Hangzhou
Print_ISBN
978-1-4244-2250-0
Type
conf
DOI
10.1109/ICCT.2008.4716206
Filename
4716206
Link To Document