title: Automated speech analysis applied to laryngeal disease categorization.
abstract: The long-term goal of the work is a decision support system for diagnostics of laryngeal diseases. Colour images of vocal folds, a voice signal, and questionnaire data are the information sources to be used in the analysis. This paper is concerned with automated analysis of a voice signal applied to screening of laryngeal diseases. The effectiveness of 11 different feature sets in classification of voice recordings of the sustained phonation of the vowel sound /a/ into a healthy and two pathological classes, diffuse and nodular, is investigated. A k-NN classifier, SVM, and a committee build using various aggregation options are used for the classification. The study was made using the mixed gender database containing 312 voice recordings. The correct classification rate of 84.6% was achieved when using an SVM committee consisting of four members. The pitch and amplitude perturbation measures, cepstral energy features, autocorrelation features as well as linear prediction cosine transform coefficients were amongst the feature sets providing the best performance. In the case of two class classification, using recordings from 79 subjects representing the pathological and 69 the healthy class, the correct classification rate of 95.5% was obtained from a five member committee. Again the pitch and amplitude perturbation measures provided the best performance.
authors.affiliation: Department of Applied Electronics, Kaunas University of Technology, LT-51368, Kaunas, Lithuania. adas.gelzinis@ktu.lt
authors.forename: A
authors.initials: A
authors.lastname: Gelzinis
authors.forename: A
authors.initials: A
authors.lastname: Verikas
authors.forename: M
authors.initials: M
authors.lastname: Bacauskiene
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007818:Laryngeal Diseases / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013064:Speech Disorders / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology; D013068:Speech Production Measurement / Q000379:methods*