Statistical modeling for dysphonic classification

Assia Ghelis, M. Guerti, C. Fredouille
{"title":"Statistical modeling for dysphonic classification","authors":"Assia Ghelis, M. Guerti, C. Fredouille","doi":"10.1109/DTIS.2010.5487588","DOIUrl":null,"url":null,"abstract":"The objective of our work is to develop an automatic system to evaluate Arabic/French dysphonic classification by modeling speech signals using a statistical modeling based on a Gaussian Mixture Model (GMM), which is state of art in speaker recognition. Speakers were conducted at Annaba University Hospital center (CHU) in the ENT service in the presence of a group composed of 8 medical specialists. Results of the experiment show that an automatic system is able to identify dysphonic speakers with an acceptable performance either in French or in Arabic language.","PeriodicalId":423978,"journal":{"name":"5th International Conference on Design & Technology of Integrated Systems in Nanoscale Era","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"5th International Conference on Design & Technology of Integrated Systems in Nanoscale Era","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DTIS.2010.5487588","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The objective of our work is to develop an automatic system to evaluate Arabic/French dysphonic classification by modeling speech signals using a statistical modeling based on a Gaussian Mixture Model (GMM), which is state of art in speaker recognition. Speakers were conducted at Annaba University Hospital center (CHU) in the ENT service in the presence of a group composed of 8 medical specialists. Results of the experiment show that an automatic system is able to identify dysphonic speakers with an acceptable performance either in French or in Arabic language.
语音障碍分类的统计建模
我们的工作目标是开发一个自动系统,通过使用基于高斯混合模型(GMM)的统计建模语音信号来评估阿拉伯语/法语语音分类,这是目前最先进的说话人识别方法。演讲者在安纳巴大学医院中心(CHU)的耳鼻喉科进行,由8名医学专家组成的小组在场。实验结果表明,该自动识别系统能够在法语或阿拉伯语中识别出发音困难的说话者,并且表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信