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Most of the existing systems and methods for laryngeal pathology detection are characterized by a classification error. One of the basic problems is the approximation and estimation of the probability density functions of the given classes. In order to increase the accuracy of laryngeal pathology detection and to eliminate the most dangerous error--classification of a patient with laryngeal disease as a normal speaker--here an approach based on modeling of the probability density functions (pdf's) of the input vectors of the normal and pathological speakers by means of two prototype distribution maps (PDM), respectively, is proposed. The pdf of the input vectors of an unknown normal or pathological speaker is also modeled by such a prototype distribution neural map--PDM(X)--and the pathology detection is done by means of a ratio of specific similarities rather than by a direct comparison of some type of distance/similarity with a threshold. The experiments show an increased classification accuracy and that the proposed method can be used for screening the laryngeal diseases. The method is applied in a consulting system for clinical practice.

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The presence of singer’s high formant (SHF) in singers of different musical styles was investigated. The voices of 10 opera, 8 folk, 4 pop singers and 53 (nonsinging) controls were recorded. The vowel ‘a’ was sung in three different registers. The percent of energy in the range of SHF and the stability of the pitch period generation were evaluated. The signals of all the singers are characterized by very high stability of the pitch period generation (> 99%), but their formant structures differ considerably. Energy concentration in the range of SHF is 23, 16, 7 and < 4% for opera, folk and pop singers and the controls, respectively.

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