Computers in Biology and Medicine
Automated speech analysis applied to laryngeal disease categorization
Computer Methods and Programs in Biomedicine
COMPARING ANNs AND GENETIC PROGRAMMING FOR VOICE QUALITY ASSESSMENT POST-TREATMENT
Applied Artificial Intelligence
Using the patient's questionnaire data to screen laryngeal disorders
Computers in Biology and Medicine
Glottal Source biometrical signature for voice pathology detection
Speech Communication
Optimal feature selection for the assessment of vocal fold disorders
Computers in Biology and Medicine
A comparison of multiple classification methods for diagnosis of Parkinson disease
Expert Systems with Applications: An International Journal
Combining image, voice, and the patient's questionnaire data to categorize laryngeal disorders
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Support vector machines applied to the detection of voice disorders
NOLISP'05 Proceedings of the 3rd international conference on Non-Linear Analyses and Algorithms for Speech Processing
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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, an approach based on modeling of the probability density functions (pdfs) 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.