The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Metadata Harvesting (OAI2)
  • Digital Archiving Policy
  • Promote your Publication

IJACSA

  • About the Journal
  • Call for Papers
  • Author Guidelines
  • Fees/ APC
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Guidelines
  • Fees
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Subscribe

Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Evaluating the Accuracy of Models for Predicting the Speech Acceptability for Children with Cochlear Implants

Author 1: Haewon Byeon

Download PDF

Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0120203

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 2, 2021.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: This study developed a model for predicting healthy hearing people’s speech acceptability for children with cochlear implants using multiple regression analysis, support vector regression, and random forest and evaluated the prediction performance of the model by comparing mean absolute errors and root mean squared errors. This study targeted 91 hearing-impaired children between four and eight years old who had worn cochlear implants at least one year and less than five years. Speech data of children wearing cochlear implants (CI) were collected through two tasks: speaking and reading. The outcome variable, healthy hearing people’s speech acceptability for children wearing CI was evaluated by 80 college students (freshman and sophomore) who did not have prior knowledge of children with a cochlear implant. The results of this study showed that the random forest algorithm (mean absolute errors=0.81and root mean squared error=0.108) was the best model for predicting the speech acceptability of children wearing CI. The results of this study imply that the predictive performance of random forest will be the best among ensemble models when developing a machine learning model using speech data of children wearing CI.

Keywords: Cochlear implants; speech acceptability; support vector regression; random forest; mean absolute errors

Haewon Byeon, “Evaluating the Accuracy of Models for Predicting the Speech Acceptability for Children with Cochlear Implants” International Journal of Advanced Computer Science and Applications(IJACSA), 12(2), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120203

@article{Byeon2021,
title = {Evaluating the Accuracy of Models for Predicting the Speech Acceptability for Children with Cochlear Implants},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120203},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120203},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {2},
author = {Haewon Byeon}
}


IJACSA

Upcoming Conferences

Future of Information and Communication Conference (FICC) 2023

2-3 March 2023

  • Virtual

Computing Conference 2023

22-23 June 2023

  • London, United Kingdom

IntelliSys 2023

7-8 September 2023

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2023

2-3 November 2023

  • San Francisco, United States
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. Registered in England and Wales. Company Number 8933205. All rights reserved. thesai.org