Acoustic analysis assessment in speech pathology detection

• Main Authors: Panek Daria, Skalski Andrzej, Gajda Janusz, Tadeusiewicz Ryszard
• Format: Article
• Language: English
• Published: Sciendo 2015-09-01
• Series: International Journal of Applied Mathematics and Computer Science
• Subjects: linear pca; non-linear pca; auto-associative neural network; validation; voice pathology detection
• Online Access: https://doi.org/10.1515/amcs-2015-0046

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