J. Minelga “Computational intelligence methods for non-invasive larynx pathology screening” doctoral thesis defence

Author, Institution: Jonas Minelga, Kaunas University of Technology

Science area, field of science: Technological Sciences, Informatics Engineering 07T

Scientific Supervisor: Prof. Dr. Habil. Antanas Verikas (Kaunas University of Technology, Technological Sciences, Informatics Engineering, 07T).

Dissertation Defence Board of Informatics Engineering Science Field:
Prof. Dr. Robertas Damaševičius (Kaunas University of Technology, Technological Sciences, Informatics Engineering, 07T) chairman,
Assoc. Prof. Dr. Nikolaj Goranin (Vilnius Gediminas Technical University, Technological Sciences, Informatics Engineering, 07T),
Prof. Dr. Rytis Maskeliūnas Kaunas University of Technology, Technological Sciences, Informatics Engineering, 07T),
Prof. Dr. Alfonsas Vainoras (Lithuanian University of Health Sciences, Biomedical Sciences, Medicine, 06B),
Dr. Marcin Wozniak (Silesian University of Technology, Technological Sciences, Informatics Engineering, 07T).

The doctoral dissertation is available at the libraries of Kaunas University of Technology (K. Donelaičio St. 20, Kaunas) and Vilnius Gediminas Technical University (Saulėtekio al. 14, Vilnius).

Annotation:

In this research techniques are analyzed and new methods are proposed for non-invasive voice pathology detection. Such voice signal parameters as Mel-frequency cepstral coefficients, autocorrelation, shape of signal envelope and patient questionnaire data such as age, sex, smoking, voice quality self evaluation and other were used to improve classification accuracy of voice pathology detection. New data dependent random forest technique and association rules based classification methods were proposed for voice signal parameters and questionnaire data classification. Classification results of voice signal parameters and questionnaire data separately and together revealed, that highest classification accuracy can be received by using both types of data together. Experiments also showed that voice pathology can be successfully detected using only questionnaire data. Proposed data dependent random forest technique allowed to achieve statistically significant classification accuracy improvement.