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

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.