Acoustic analysis assessment in speech pathology detection

• Autorzy: Panek D. , Skalski A. , Gajda J. , Tadeusiewicz R.

Identyfikatory

DOI

10.1515/amcs-2015-0046

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

linear PCA; nonlinear PCA; autoassociative neural network; validation; voice pathology detection

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2015
• Tom: Vol. 25, no. 3
• Strony: 631--643
• Opis fizyczny: Bibliogr. 53 poz., rys., tab., wykr.

Twórcy

autor

Panek D.

• Department of Measurement and Electronics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland

autor

Skalski A.

• Department of Measurement and Electronics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland

autor

Gajda J.

• Department of Measurement and Electronics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland

autor

Tadeusiewicz R.

• Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland

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