Computer aided tool for diagnosis of ENT pathologies using digital signal processing of speech and stroboscopic images

Abstract

The development of computer software and other technologies greatly facilitates the evaluation of pathological voice patients. This fact allows to reduce exploration time, improves the reproducibility of results and creates the possibility of test protocol standardization needed for the intercommunication between the different voice specialists. The proposed application encompasses the most important aspects which should be taken into account regarding dysphonic patients. It is a multidimensional scope which involves subjective questionnaires and perceptual, aerodynamic, acoustic and stroboscopic evaluations. In this system, the authors have designed and created simple tools for recording and automatic acoustic analysis for the acquisition and edition of stroboscopic images. The purpose is to work with all necessary tools running on a single application, without having to export and import data from other computer programs. Therefore, the objective is to synthetize the basic voice and the exploration of the vocal folds, simplifying it through the design of a program which helps us to analyze step-by-step each aspect of the vocal pathology. The evaluation of the tool has been performed by the otolaryngologists through periodical (medical) appointments on 25 patients for one year a year, and the results are promising either for the professionals as well as for the patients which receive a detailed report with the objective information concerning the features of their voice and vocal cords.

FATFA-based DRN: Feedback Artificial Tree Firefly Algorithm-Enabled Deep Residual Network for Pathological Speech Enhancement

“…In recent times, deep learning techniques are widely employed in the speech recognition, and the image processing areas [25]. Nowadays, deep learning has a progressive growth in the quantity and superiority of pathological voice judgment [26]. Hence, deep learning techniques have the capability for solving the pathological voice classification problems [7].…”