Abstract

Objective: The present study was carried out to explore the tone production ability of the Mandarin-speaking children with cochlear implants (CI) by using an artificial neural network model and to examine the potential contributing factors underlining their tone production performance. The results of this study might provide useful guidelines for post-operative rehabilitation processes of pediatric CI users. Methods: Two hundred and seventy-eight prelingually deafened children who received unilateral CI participated in this study. As controls, 170 similarly-aged children with normal hearing (NH) were recruited. A total of 36 Chinese monosyllabic words were selected as the tone production targets. Vocal production samples were recorded and the fundamental frequency (F0) contour of each syllable was extracted using an auto-correlation algorithm followed by manual correction. An artificial neural network was created in MATLAB to classify the tone production. The relationships between tone production and several demographic factors were evaluated. Results: Pediatric CI users produced Mandarin tones much less accurately than did the NH children (58.8% vs. 91.5% correct). Tremendous variability in tone production performance existed among the CI children. Tones 2 and 3 were produced less accurately than tones 1 and 4 for both groups. For the CI group, all tones when in error tended to be judged as tone 1. The tone production accuracy was negatively correlated with age at implantation and positively correlated with CI use duration with correlation coefficients (r) of -0.215 (P=0.003) and 0.203 (P=0.005), respectively. Age was one of the determinants of tonal ability for NH children. Conclusions: For children with severe to profound hearing loss, early implantation and persistent use of CI are beneficial to their tone production development. Artificial neural network is a convenient and reliable assessment tool for the development of tonal ability of hearing-impaired children who are in the rehabilitation processes that focus on speech and language expression.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call