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Advisor(s)
Abstract(s)
Using acoustic analysis to classify and identify speech disorders noninvasively
can reduce waiting times for patients and specialists while also increasing
the accuracy of diagnoses. In order to identify models to use in a vocal disease
diagnosis system, we want to know which models have higher success rates in
distinguishing between healthy and pathological sounds. For this purpose, 708
diseased people spread throughout 19 pathologies, and 194 control people were
used. There are nine sound files per subject, three vowels in three tones, for each
subject. From each sound file, 13 parameters were extracted. For the classification
of healthy/pathological individuals, a variety of classifiers based on Machine
Learning models were used, including decision trees, discriminant analyses, logistic
regression classifiers, naive Bayes classifiers, support vector machines, classifiers
of closely related variables, ensemble classifiers and artificial neural network
classifiers. For each patient, 118 parameters were used initially. The first analysis
aimed to find the best classifier, thus obtaining an accuracy of 81.3% for the
Ensemble Sub-space Discriminant classifier. The second and third analyses aimed
to improve ground accuracy using preprocessingmethodologies. Therefore, in the
second analysis, the PCA technique was used, with an accuracy of 80.2%. The
third analysis combined several outlier treatment models with several data normalizationmodels
and, in general, accuracy improved, obtaining the best accuracy
(82.9%) with the combination of the Greebs model for outliers treatment and the
range model for the normalization of data procedure.
Description
Keywords
Outliers Normalization Speech Pathologies Speech Features Machine Learning Vocal Acoustic Analysis
Citation
Fernandes, Joana Filipa Teixeira; Freitas, Diamantino Rui; Teixeira, João Paulo (2024). Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 1, p. 287–299. ISBN 978-3-031-53024-1.
Publisher
Springer Nature