Voice pathology detection by fuzzy logic

Abstract

In this paper an efficient feature extraction methods and fuzzy logic based disorder assessment technique were used to investigate voice signals of patients suffering from functional dysphonia, hyperfunctional dysphonia, vocal cord paralysis and laryngitis. In this work, a vector made up from 28 acoustic parameters was an input for Principal Component Analysis, kernel Principal Component Analysis and Auto-associative Neural Network. Using S-shaped membership function of fuzzy logic, signals were clustered into 2 classes -healthy and pathology one. The amount of fuzzy membership of normal and pathological voice signals in their corresponding clusters was a measure to quantify the membership of the features of a particular class. In the end, S-shaped fuzzy logic method was used as a way of voice pathology detection. A classification accuracy up to 100 percent was achieved using initial 28 feature vector.This full text paper was peer-reviewed at the direction of IEEE Instrumentation and Measurement Society prior to the acceptance and publication.978-1-4799-6144-6/15/$31.00 ©2015 IEEE

Gender classification based on voice signals using fuzzy models and optimization algorithms

“…Se calcula como la media ponderada de frecuencias, determinada mediante la transformada de Fourier, con sus magnitudes en el histograma de la señal. Se calcula mediante la ecuación (14).…”

“…Otros propósitos del uso de señales de voz es la formulación e implementación de sistemas para la distinción del habla [7]- [9], reconocimiento del habla [10], reconocimiento de lenguaje [11], reconocimiento de emociones basados en habla y en género [12], [13], diagnóstico de enfermedades patológicas como la disfonía y la laringitis [14], diagnóstico de nódulos, edemas y parálisis unilateral de las cuerdas vocales [15], reconocimiento de disartria en sistemas de reconocimiento automático de habla [16] y la detección temprana de Parkinson mediante voz [17].…”