- Tytuł:
- Acoustic analysis assessment in speech pathology detection
- Autorzy:
-
Panek, D.
Skalski, A.
Gajda, J.
Tadeusiewicz, R. - Powiązania:
- https://bibliotekanauki.pl/articles/329710.pdf
- Data publikacji:
- 2015
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
linear PCA
nonlinear PCA
autoassociative neural network
validation
voice pathology detection - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2015, 25, 3; 631-643
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł