首页> 外文期刊>BioMedical Engineering OnLine >Classification of voice disorder in children with cochlear implantation and hearing aid using multiple classifier fusion
【24h】

Classification of voice disorder in children with cochlear implantation and hearing aid using multiple classifier fusion

机译:多分类器融合对人工耳蜗和助听器儿童语音障碍的分类

获取原文
           

摘要

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.
机译:背景技术通过在人工耳蜗植入后或使用助听器获得音频反馈,儿童的语音产生和语音语音特征逐渐得到改善。这项研究的目的是开发和评估人工耳蜗植入和助听器儿童语音障碍的自动分类。方法我们使用以下定义考虑了儿童语音中的四种障碍类别:级别_1:产生自发发声并自发地和模仿地使用单词的孩子。级别_2:产生自发发声的孩子自发使用单词并模仿地简短句子。 Level_3:产生自发发声的孩子自发使用单词和任意句子。 Level_4:没有任何听力损失背景的正常儿童。三十名波斯儿童参加了这项研究,其中包括从一到三级的六个孩子,以及在四级中的12个孩子。分析了五个孤立的波斯语单词“ mashin”,“ mar”,“ moosh”,“ gav”和“ mouz”的语音样本。考虑了四个级别的语音质量,级别越高,语音障碍的重要性就越小。从语音信号中提取“基于帧”和“基于单词”的功能。基于帧的特征包括强度,基频,共振峰,鼻音和近似熵,而基于词的特征包括相空间特征和小波系数。对于基于框架的特征,使用隐藏的马尔可夫模型作为分类器,对于基于单词的特征,使用神经网络。结果采用多数投票规则,线性组合和堆叠融合三种方法对分类器进行融合后,使用基于帧和基于单词的特征以及MVR规则(级别1:100%,级别2:93.75%,级别)可以实现最佳分类率3:100%,级别4:94%)。结论这项研究的结果可能有助于语音病理学家对年龄相同的人工耳蜗植入或助听器患儿的语音障碍恢复情况进行随访。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号