There are a lot of papers on automatic classification between normal and pathological voices, but they have the lack in the degree of severity estimation of the identified voice disorders. Building a model of pathological and normal voices identification, that can also evaluate the degree of severity of the identified voice disorders among students. In the present work, we present an automatic classifier using acoustical measurements on registered sustained vowels /a/ and pattern recognition tools based on neural networks. The training set was done by classifying students' recorded voices based on threshold from the literature. We retrieve the pitch, jitter, shimmer and harmonic-to-noise ratio values of the speech utterance /a/, which constitute the input vector of the neural network. The degree of severity is estimated to evaluate how the parameters are far from the standard values based on the percent of normal and pathological values. In this work, the base data used for testing the proposed algorithm of the neural network is formed by healthy and pathological voices from German database of voice disorders. The performance of the proposed algorithm is evaluated in a term of the accuracy (97.9%), sensitivity (1.6%), and specificity (95.1%). The classification rate is 90% for normal class and 95% for pathological class.

Objective: Communication disorders negatively affect the academic curriculum for students in higher education. Acoustic analysis is an objective leading tool to describe these disorders; however the amount of the acoustic parameters makes differentiating pathological voices among healthy ones not an easy task. The purpose of the present paper was to present the relevant acoustic parameters that differentiate objectively pathological voices among healthy ones. Methods: Pathological and normal voices samples of /a/, /i/ and /u/ utterances, of 400 students were recorded and analyzed acoustically with PRAAT software, then a feature of acoustic parameters were extracted. A statistical analysis was performed in order to reduce the extracted parameters to main relevant ones in order to build a model that will be the basis for the objective diagnostic. Results: Mean amplitude, jitter local absolute, second bandwidth of the second formant and Noise-to-Harmonic Ratio; are relevant acoustic parameters that characterize pathological voices among healthy ones, for the utterances of vowels /a/, /i/ and /u/ Thresholds of the acoustic parameters of pathological /a/, /i/, and /u/ were calculated. A training model was built and simulated on Matlab, and a comparison between Hidden Markov Model and K-Nearest Neighbors classification methods were done (Hidden Markov Model had a rate of recognition of 95% and K-Nearest Neighbors within the reduced acoustic parameters reached a recognition rate of 97%). Conclusion: Through the identified parameters, we can objectively detect pathological voices among healthy ones for diagnostic purposes. As a future work, the present approach is an attempt toward identifying acoustic parameters that characterize each voice disorder.