Algorithm of Acoustic Analysis of Communication Disorders within Moroccan Students
- *Corresponding Author:
- Brahim Sabir
University Hassan II Mohammedia
Faculty of Science Ben M’Sik Casablanca, Morocco
E-mail: [email protected]
Received date: January 08, 2016 Accepted date: January 18, 2016 Published date: January 25, 2016
Citation: Sabir B, Touri B, Moussetad M (2016) Algorithm of Acoustic Analysis of Communication Disorders within Moroccan Students. Commun Disord Deaf Stud Hearing Aids 4:149. doi: 10.4172/2375-4427.1000149
Copyright: © 2016 Sabir B, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.