Acoustic analysis of pathological voices. A voice analysis systemfor the screening of laryngeal diseases

The authors propose a software system for pathological voice analysis and screening of laryngeal diseases that has the following properties: a) The analysis is totally noninvasive-the microphone is about 30 cm from the patient's mouth, and contact microphones or laryngophones are not used. b) The system is built around a low-cost personal computer (PC). The additional hardware consists of a standard sound card such as “Sound Blaster” (Creative Technology Inc., Paris, France). The card also includes a microphone. Any other analog-to-digital converter board allowing a sampling rate higher than 16 kHz, with 16 bit resolution (Sound Blaster and OROS have been already tested), and a linear phase condenser or electret microphone can be used to minimize the distortions during capture of the signal. c) The software is graphics-driven and user-friendly, therefore no special training is needed in using the system. d) The system is electrically safe since there is no electrical link between patient and PC     

Robust hybrid pitch detector

A hybrid pitch detector characterised by parallel analysis of the speech signal in temporal, spectral and cepstral domains is proposed. The voiced/unvoiced decision and pitch period evaluation is realised by a logical analysis of the results from three domains. The experimental analysis shows the robustness of the detector for noisy and telephone speech.<>     

Laryngeal pathology detection by means of class-specific neuralmaps

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