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Speech Assistance for Persons With Speech Impediments Using Artificial Neural Networks

[+] Author Affiliations
Ramy Mounir, Redwan Alqasemi, Rajiv Dubey

University of South Florida, Tampa, FL

Paper No. IMECE2017-71027, pp. V003T04A056; 7 pages
  • ASME 2017 International Mechanical Engineering Congress and Exposition
  • Volume 3: Biomedical and Biotechnology Engineering
  • Tampa, Florida, USA, November 3–9, 2017
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5836-3
  • Copyright © 2017 by ASME


This work focuses on the research related to enabling individuals with speech impairment to use speech-to-text software to recognize and dictate their speech. Automatic Speech Recognition (ASR) tends to be a challenging problem for researchers because of the wide range of speech variability. Some of the variabilities include different accents, pronunciations, speeds, volumes, etc. It is very difficult to train an end-to-end speech recognition model on data with speech impediment due to the lack of large enough datasets, and the difficulty of generalizing a speech disorder pattern on all users with speech impediments. This work highlights the different techniques used in deep learning to achieve ASR and how it can be modified to recognize and dictate speech from individuals with speech impediments.

Copyright © 2017 by ASME



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