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2015 | 25 | 3 | 631-643
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Acoustic analysis assessment in speech pathology detection

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