Abstract
Background
Speech production and speech phonetic features gradually improve in children by obtaining audio feedback after cochlear implantation or using hearing aids. The aim of this study was to develop and evaluate automated classification of voice disorder in children with cochlear implantation and hearing aids.
Methods
We considered 4 disorder categories in children’s voice using the following definitions:
Level_1: Children who produce spontaneous phonation and use words spontaneously and imitatively.
Level_2: Children, who produce spontaneous phonation, use words spontaneously and make short sentences imitatively.
Level_3: Children, who produce spontaneous phonations, use words and arbitrary sentences spontaneously.
Level_4: Normal children without any hearing loss background. Thirty Persian children participated in the study, including six children in each level from one to three and 12 children in level four. Voice samples of five isolated Persian words “mashin”, “mar”, “moosh”, “gav” and “mouz” were analyzed. Four levels of the voice quality were considered, the higher the level the less significant the speech disorder. “Frame-based” and “word-based” features were extracted from voice signals. The frame-based features include intensity, fundamental frequency, formants, nasality and approximate entropy and word-based features include phase space features and wavelet coefficients. For frame-based features, hidden Markov models were used as classifiers and for word-based features, neural network was used.
Results
After Classifiers fusion with three methods: Majority Voting Rule, Linear Combination and Stacked fusion, the best classification rates were obtained using frame-based and word-based features with MVR rule (level 1:100%, level 2: 93.75%, level 3: 100%, level 4: 94%).
Conclusions
Result of this study may help speech pathologists follow up voice disorder recovery in children with cochlear implantation or hearing aid who are in the same age range.
Speech production and speech phonetic features gradually improve in children by obtaining audio feedback after cochlear implantation or using hearing aids. The aim of this study was to develop and evaluate automated classification of voice disorder in children with cochlear implantation and hearing aids.
Methods
We considered 4 disorder categories in children’s voice using the following definitions:
Level_1: Children who produce spontaneous phonation and use words spontaneously and imitatively.
Level_2: Children, who produce spontaneous phonation, use words spontaneously and make short sentences imitatively.
Level_3: Children, who produce spontaneous phonations, use words and arbitrary sentences spontaneously.
Level_4: Normal children without any hearing loss background. Thirty Persian children participated in the study, including six children in each level from one to three and 12 children in level four. Voice samples of five isolated Persian words “mashin”, “mar”, “moosh”, “gav” and “mouz” were analyzed. Four levels of the voice quality were considered, the higher the level the less significant the speech disorder. “Frame-based” and “word-based” features were extracted from voice signals. The frame-based features include intensity, fundamental frequency, formants, nasality and approximate entropy and word-based features include phase space features and wavelet coefficients. For frame-based features, hidden Markov models were used as classifiers and for word-based features, neural network was used.
Results
After Classifiers fusion with three methods: Majority Voting Rule, Linear Combination and Stacked fusion, the best classification rates were obtained using frame-based and word-based features with MVR rule (level 1:100%, level 2: 93.75%, level 3: 100%, level 4: 94%).
Conclusions
Result of this study may help speech pathologists follow up voice disorder recovery in children with cochlear implantation or hearing aid who are in the same age range.
| Original language | English |
|---|---|
| Journal | BioMedical Engineering Online |
| Volume | 10 |
| Issue number | 3 |
| Number of pages | 18 |
| ISSN | 1475-925X |
| DOIs | |
| Publication status | Published - 2011 |
| Externally published | Yes |
Bibliographical note
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.
