Publications:Exploring Kernels in SVM-Based Classification of Larynx Pathology from Human Voice
From ISLAB/CAISR
| Title | Exploring Kernels in SVM-Based Classification of Larynx Pathology from Human Voice |
|---|---|
| Author | Evaldas Vaiciukynas and Adas Gelzinis and Marija Bacauskiene and Antanas Verikas and Aurelija Vegiene |
| Year | 2010 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Proceedings of the 5th International Conference on Electrical and Control Technologies ECT-2010, May 6-7, 2010, Kaunas, Lithuania |
| Conference | The 5th International Conference on Electrical and Control Technologies 6-7 May 2010, Kaunas, Lithuania |
| DOI | |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:352842 |
| Abstract | 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 further approximated, using 3 strategies: sampling, averaging, and estimation. SVM and LS-SVM categorize pre-processed data into normal, nodular, and diffuse classes. Since it is a three-class problem, various combination schemes are explored. Constructed custom kernels outperformed a popular non-linear RBF kernel. Features, estimated with GMM, and SVM kernels, designed to exploit this information, is an interesting fusion of probabilistic and discriminative models for human voice-based classification of larynx pathology. |
