An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features, linear discriminant analysis and support vector machine

A New Approach for Detection of Pathological Voice Disorders with Reduced Parameters

“…It is concluded from the experimental results that multi-class SVMs provide good performance for classification of these voice data From the previous studies, [34][35][36][37][38] that showed that the related voices could be classified into normal / pathological as depending on sounds' characteristic features. On the other hand, their used classifier and methods, in [34] as accuracy rate 98.3%, in [35] as accuracy rate 97.01%, in [36] as accuracy rate 100%, in [37] as accuracy rate 94.26%, and in [38] as accuracy rate 98.23%. However, in this study we not only have increased accuracy rate of correct class for pathological and normal classification, but also are able to classify the related voices as four different classes, which are important for diagnosying speech voice' analysis.…”

Análisis de la influencia de las técnicas de compresión de voz en la detección de anomalías vocales

“…En [13] se presenta un sistema no invasivo con una tasa de acierto del 99.44%, usando bancos de filtros de coeficientes cepstrales en escala de Mel (MFCC) y un clasificador HMM (Hidden Markov Model); la base de datos utilizada fue la Massachusetts Eye and Ear Infirmary (MEII). En [14] se propone un sistema que hace uso de la transformada de paquetes de wavelets (WPT) para parametrización, y de LDA y PCA (Principal Component Analysis) como métodos de clasificación, que optimiza el proceso de detección automática de patologías en señales de voz; los autores reportan una precisión del 100%.…”

A Hybrid of Genetic Algorithm and Support Vector Machine for Feature Reduction and Detection of Vocal Fold Pathology

“…Therefore, it uses a signalrepresentation criterion to perform dimension reduction while preserving much of the rando mness or variance in the high-dimensional space as possible [20].…”

“…in N-dimensional space by a linear co mb ination of M (M < N) independent vectors is obtained by projecting the random vector X into the eigenvectors corresponding to the largest eigenvalues of the covariance mat rix of vector X [20].…”