ESPINOLA, S. B.; http://lattes.cnpq.br/716753158335685; ESPINOLA, Sérgio de Brito.
Résumé:
It is estimated one-third of the work force relies on the use the voice in their jobs. The clinical diagnostic may be performed on voice listening by a specialist (subjective perspective) or through invasive and often not cheaper exams to
check vocal structures. The area of Voice Acoustic analyses aims to extract
robust measurements to describe several phenomena associated with voice
production, or human being particular characteristics like fundamental frequency,
timbre, etc. This study consisted of a model characterizing the digital voice
processing for support in building automatic systems for the identification of
disorders of speech (to aid diagnosis of pathologies). To support this
investigation and proposed model, a commercial voice database (KAY base) was
used with the endorsement from medical specialists. Derived acoustic analyses of
those speech samples data records were presented to professionals for
classification and six “severities groups” case-studied were built. After these
analyses, one Normal group was added and, at the end, 182 voices have been
selected. Their refined audio database contain, among other things, an indexed
list of vocal descriptors calculated on the presence of the utterance of the vowel
\a\ sustained speech. Statistical evidences were found: a) Difference between
pathological groups vocal descriptors to normal (expected); b) It was achieved
100% from true positive, most cases, among Paralysis, Reinke's Edema and
Nodules separations; c) from few cases, there were detected minor distinctions:
Paralysis, Reinke's Edema, Nodules and Edema (pair comparison) with
disordered groups; c) Among Machine Learning Algorithms (artificial neural
networks "RN" and support vector machine "SVM"), the technique of Principal
Components Analyses (PCA) and main statistics performed, it was found facts to
help to structure some automated recognition systems. These Supervised
learning methods showed that it could be possible to generate classification
predictions (disordered presence) for the response to new data; and d) Inner
tests also confirmed literature established reference thresholds. Hence
considering suitable combinations of descriptors with two machine learning
classifiers, as showed, is sufficient suitable and worthy.