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Acoustic analysis assessment in speech pathology detection

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Panek D., Skalski A., Gajda J., Tadeusiewicz R.

International Journal of Applied Mathematics and Computer Science

2015

tom 25

nr 3

631-643

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.

Ograniczanie wyników

1 International Journal of Applied Mathematics and Computer Science

1 Gajda J.

1 Panek D.

1 Skalski A.

1 Tadeusiewicz R.

1 2015

Wyniki wyszukiwania

Acoustic analysis assessment in speech pathology detection