ACOUSTIC ANALYSIS ASSESSMENT IN SPEECH PATHOLOGY DETECTION

作者：Panek Daria^*; Skalski Andrzej; Gajda Janusz; Tadeusiewicz Ryszard

来源：International Journal of Applied Mathematics and Computer Science, 2015, 25(3): 631-643.

DOI：10.1515/amcs-2015-0046

• linear PCA
• non-linear PCA
• auto-associative neural network
• validation
• voice pathology detection

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.

• 出版日期2015-8