Developing A Model for Predicting the Speech Intelligibility of South Korean Children with Cochlear Implantation using a Random Forest Algorithm

Abstract

The random forest technique, a tree-based study model, predicts the results by using random decision trees based on the bootstrap technique. Therefore, it has a high prediction power and fewer errors, which are advantages of this method. This study aimed to provide baseline data regarding the language therapy after cochlear implantation by identifying the factors associated with the speech intelligibility of children with cochlear implantation. This study evaluating the factors associated with the articulation accuracy of children with cochlear implantation. This study targeted 82 hearing-impaired children, who lived in Seoul, Incheon, and Suwon areas, were between 4 and 8 years old, and had been worn cochlear implant at least one year and less than five years. Explanatory variables used in this study included gender, age, household income, the wear time of a cochlear implant, vocabulary index, and corrected hearing. Speech intelligibility was analyzed using the 'speech intelligibility test tool' composed of nine sentences. The predictive model for speech intelligibility of children with cochlear implants was developed using random forest. The major predictors of the articulation accuracy of children with cochlear implantation were the wear time of a cochlear implant, the time since cochlear implantation, vocabulary, household income, age, and gender, in the order of the magnitude. The final error rate of the random forest model developed by generating 500 bootstrap samples was 0.22, and the prediction rate was 78.8%. The results of this study on a prediction model suggested that it would be necessary to implement cochlear implantation and to develop a customized aural rehabilitation program considering the linguistic ability of a subject for enhancing the speech intelligibility of a child with cochlear implantation.

Authors and Affiliations

Haewon Byeon

Keywords

Related Articles

Creating a Knowledge Database for Lectures of Faculty Members, Proposed E-Module for Isra University

Higher education in Jordan is currently expanding as new universities open and compete for offering the best learning experience. Many universities face accreditation challenges, hence, they attend to recruit lecturers w...

TERRAIN COVERAGE ANT ALGORITHMS: THE RANDOM KICK EFFECT

In this work the effect of random repositioning of ant robots/agents on the performance of terrain coverage algorithms is investigated. A number of well-known terrain coverage algorithms are implemented and studied in a...

Detection and Removal of Gray, Black and Cooperative Black Hole Attacks in AODV Technique

Mobile ad hoc network (MANET) is an autonomous self-configuring infrastructure-less wireless network. MANET is vulnerable to a lot of routing security threats due to unreliability of its nodes that are highly involved in...

Audio Search Based on Keyword Spotting in Arabic Language

Keyword spotting is an important application of speech recognition. This research introduces a keyword spotting approach to perform audio searching of uttered words in Arabic speech. The matching process depends on the u...

Mobile Learning Application Development for Improvement of English Listening Comprehension

Trend towards English language learning has been increased because it is considered as Lingua franca i.e. language of communication for all. However students of Pakistan are behind in this pace, especially rural elementa...

Download PDF file
  • EP ID EP417602
  • DOI 10.14569/IJACSA.2018.091113
  • Views 115
  • Downloads 0

How To Cite

Haewon Byeon (2018). Developing A Model for Predicting the Speech Intelligibility of South Korean Children with Cochlear Implantation using a Random Forest Algorithm. International Journal of Advanced Computer Science & Applications, 9(11), 88-93. https://europub.co.uk/articles/-A-417602