论文信息 - Exploring Kernels in SVM-Based Classification of Larynx Pathology from Human Voice

Exploring Kernels in SVM-Based Classification of Larynx Pathology from Human Voice

In this paper identification of laryngeal disorders using cepstral parameters of human voice is investigated. Mel-frequency cepstral coefficients (MFCC), extracted from audio recordings, are furthe ...

[1] Pedro Gómez Vilda,et al. Dimensionality Reduction of a Pathological Voice Quality Assessment System Based on Gaussian Mixture Models and Short-Term Cepstral Parameters , 2006, IEEE Transactions on Biomedical Engineering.

[2] Joost van Doremalen. Hierarchical Temporal Memory Networks for Spoken Digit Recognition , 2007 .

[3] Wenming Zheng. Class-Incremental Generalized Discriminant Analysis , 2006, Neural Computation.

[4] Johan A. K. Suykens,et al. Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[5] Jason Weston,et al. Multi-Class Support Vector Machines , 1998 .

[6] Lior Wolf,et al. Kernel principal angles for classification machines with applications to image sequence interpretation , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[7] Elias Pampalk. A Matlab Toolbox to Compute Music Similarity from Audio , 2004, ISMIR.

[8] Antanas Verikas,et al. Automated speech analysis applied to laryngeal disease categorization , 2008, Comput. Methods Programs Biomed..

[9] Peter J. Bickel,et al. The Earth Mover's distance is the Mallows distance: some insights from statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.