@misc{Panek_Daria_Acoustic,
 author={Panek, Daria and Skalski, Andrzej and Gajda, Janusz (1933- ) and Tadeusiewicz, Ryszard},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 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.},
 abstract={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.},
 abstract={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.},
 title={Acoustic analysis assessment in speech pathology detection},
 type={artykuł},
 keywords={linear PCA, non-linear PCA, auto-associative neural network, validation, voice pathology detection},
}