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Exploring Kernels in SVM-Based Classification of Larynx Pathology from Human Voice
Kaunas University of Technology.
Kaunas University of Technology.
Kaunas University of Technology.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.ORCID iD: 0000-0003-2185-8973
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2010 (English)In: Proceedings of the 5th International Conference on Electrical and Control Technologies ECT-2010, May 6-7, 2010, Kaunas, Lithuania, Kaunas: KUT , 2010, p. 67-72Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Kaunas: KUT , 2010. p. 67-72
Keywords [en]
Laryngeal disorder, Pathological voice, Voice processing, Mel-frequency cepstral coefficients, Sequence kernel, Principal canonical correlation, Monte-Carlo sampling, Kullback-Leibler divergence, Earth mover’s distance, GMM, SVM
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-5961Scopus ID: 2-s2.0-84941687065OAI: oai:DiVA.org:hh-5961DiVA, id: diva2:352842
Conference
The 5th International Conference on Electrical and Control Technologies 6-7 May 2010, Kaunas, Lithuania
Available from: 2010-09-23 Created: 2010-09-22 Last updated: 2018-09-05Bibliographically approved

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Verikas, Antanas

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf