Identifying Stuttering using Deep learning
Vedant Tibrewal1, Mohammed Mobasserul Haque2, Aman Pandey3, Manimozhi M4
1Vedant Tibrewal Student of Vellore Institute of Technology, 3rd-year Electronics and Communication Engineering
2Mohammed Mobasserul Haque Student of Vellore Institute of Technology, 3rd-year Electronics and Communication Engineering
3Aman Pandey Student of Vellore Institute of Technology, 3rd-year Electronics and Communication Engineering
4M. Manimozhi, Associate Professor in the department of Control and Automation, VIT University.
Manuscript received on 20 August 2019. | Revised Manuscript received on 07 September 2019. | Manuscript published on 30 September 2019. | PP: 1152-1154 | Volume-8 Issue-11, September 2019. | Retrieval Number: J90770881019/2019©BEIESP | DOI: 10.35940/ijitee.J9077.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Stuttering is a prevalent neurodevelopmental speech disorder, wherein people suffer from disfluencies in speech production. Speech disorders such as stuttering affect a variety of other communication problems such as hearing and fluency. Common therapies of stuttering involve strategies to minimize stuttering but do not attempt to eliminate stuttering, Researchers have analyzed the root cause of stuttering tends to be neurological roots. Therefore, there needs to be a more generic therapy technique which is more adaptive. This paper proposes a deep learning and neural network-based algorithm for adaptive neurological stuttering by utilizing the potential of mirror neurons.
Keywords: ANN, Audio Feedback, MFCC, Signal Processing,, Stuttered speech diagnostics,
Scope of the Article: