基于深度神经网络的病理性语音识别

Xiaojun Zhang, Zhi Tao, Heming Zhao, Tianqi Xu
{"title":"基于深度神经网络的病理性语音识别","authors":"Xiaojun Zhang, Zhi Tao, Heming Zhao, Tianqi Xu","doi":"10.1109/ICSAI.2017.8248337","DOIUrl":null,"url":null,"abstract":"The deep neural network(DNN) is used for the identification and classification of pathological voice. Time domain characteristics and frequency domain characteristics are selected. Compared with the traditional recognition algorithm. The result of experimental shows that the DNN method can effectively improve the recognition rate in both normal-pathological voice of vocal cords recognition and pathological voice of vocal cords segmentation.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"3 11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Pathological voice recognition by deep neural network\",\"authors\":\"Xiaojun Zhang, Zhi Tao, Heming Zhao, Tianqi Xu\",\"doi\":\"10.1109/ICSAI.2017.8248337\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The deep neural network(DNN) is used for the identification and classification of pathological voice. Time domain characteristics and frequency domain characteristics are selected. Compared with the traditional recognition algorithm. The result of experimental shows that the DNN method can effectively improve the recognition rate in both normal-pathological voice of vocal cords recognition and pathological voice of vocal cords segmentation.\",\"PeriodicalId\":285726,\"journal\":{\"name\":\"2017 4th International Conference on Systems and Informatics (ICSAI)\",\"volume\":\"3 11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 4th International Conference on Systems and Informatics (ICSAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSAI.2017.8248337\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 4th International Conference on Systems and Informatics (ICSAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSAI.2017.8248337","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

将深度神经网络(deep neural network, DNN)用于病理语音的识别和分类。选择时域特性和频域特性。与传统的识别算法相比较。实验结果表明,DNN方法在正常-病理声带识别和病理声带分割中都能有效提高识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pathological voice recognition by deep neural network
The deep neural network(DNN) is used for the identification and classification of pathological voice. Time domain characteristics and frequency domain characteristics are selected. Compared with the traditional recognition algorithm. The result of experimental shows that the DNN method can effectively improve the recognition rate in both normal-pathological voice of vocal cords recognition and pathological voice of vocal cords segmentation.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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:481959085
Book学术官方微信