Exploring Kernels in SVM-Based Classification of Larynx Pathology from Human Voice
Evaldas Vaičiukynas, Adas Gelžinis, Marija Bačauskienė, Antanas Verikas, Aurelija Vegienė · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2010
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.