The authors are grateful to the Foundation for Science and Technology (FCT, Portugal)
for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI
(UIDB/05757/2020 and UIDP/05757/2020), SusTEC (LA/P/0007/2021) and
2021.04729.BD.
Voice pathologies are widespread in society. However, the exams are invasive and uncomfortable for the patient, depending on the doctor’s experience doing the evaluation. Classifying and recognizing speech pathologies in a non-invasive way using acoustic analysis saves time for the patient and the specialist while allowing analyzes to be objective and efficient. This work presents a detailed description of an aid system for diagnosing speech pathologies associated with the larynx. The interface displays the parameters that physicians use most to classify subjects: absolute Jitter, relative Jitter, absolute Shimmer, relative Shimmer, Harmonic to Noise Ratio (HNR) and autocorrelation. The parameters used for the classification of the model are also presented (relative Jitter, absolute Jitter, RAP jitter, PPQ5 Jitter, absolute Shimmer, relative Shimmer, shimmer APQ3, shimmer APQ5, fundamental frequency, HNR, autocorrelation, Shannon entropy, entropy logarithmic and subject’s sex), as well as the description of the entire pre-processing of the data (treatment of Outliers using the quartile method, then data normalization and, finally, application of Principal Component Analysis (PCA) to reduce the dimension). The selected classification model is Wide Neural Network, with an accuracy of 98% and AUC of 0.99.
