Autor:
Panek, Daria ; Skalski, Andrzej ; Gajda, Janusz (1933- ) ; Tadeusiewicz, Ryszard
Współtwórca:
Byrski, Aleksander - ed. ; Kisiel-Dorohinicki, Marek - ed. ; Dobrowolski, Grzegorz - ed.
Tytuł:
Acoustic analysis assessment in speech pathology detection
Podtytuł:
Tytuł publikacji grupowej:
Temat i słowa kluczowe:
linear PCA ; non-linear PCA ; auto-associative neural network ; validation ; voice pathology detection
Abstract:
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.
Wydawca:
Zielona Góra: Uniwersytet Zielonogórski
Typ zasobu:
DOI:
Strony:
Źródło:
AMCS, volume 25, number 3 (2015) ; kliknij tutaj, żeby przejść