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Paper details
Number 3 - September 2015
Volume 25 - 2015
Acoustic analysis assessment in speech pathology detection
Daria Panek, Andrzej Skalski, Janusz Gajda, Ryszard Tadeusiewicz
Abstract
Automatic detection of voice pathologies enables non-invasive, low cost and objective assessments of the presence of
disorders, as well as accelerating and improving the process of diagnosis and clinical treatment given to patients. In
this work, a vector made up of 28 acoustic parameters is evaluated using principal component analysis (PCA), kernel
principal component analysis (kPCA) and an auto-associative neural network (NLPCA) in four kinds of pathology detection
(hyperfunctional dysphonia, functional dysphonia, laryngitis, vocal cord paralysis) using the a, i and u vowels, spoken at a high, low and normal pitch. The results indicate that the kPCA and NLPCA methods can be considered a step towards pathology detection of the vocal folds. The results show that such an approach provides acceptable results for this purpose, with the best efficiency levels of around 100%. The study brings the most commonly used approaches to speech signal processing together and leads to a comparison of the machine learning methods determining the health status of the patient.
Keywords
linear PCA, non-linear PCA, auto-associative neural network, validation, voice pathology detection