alexa Abstract | Algorithm of Acoustic Analysis of Communication Disorders within Moroccan Students
ISSN: 2375-4427

Journal of Communication Disorders, Deaf Studies & Hearing Aids
Open Access

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Research Article Open Access

Abstract

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.

To read the full article Peer-reviewed Article PDF image | Peer-reviewed Full Article image

Author(s): Brahim Sabir, Bouzekri Touri and Mohamed Moussetad

Keywords

Communication disorders, Acoustic analysis, PRAAT, Classification methods, Social Communication Disorders

Share This Page

Additional Info

Loading
Loading Please wait..
 
 
 
Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords