基于深度神经网络的病理性语音识别

2017 4th International Conference on Systems and Informatics (ICSAI) Pub Date : 2017-11-01 DOI:10.1109/ICSAI.2017.8248337

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。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

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

2017 4th International Conference on Systems and Informatics (ICSAI)

自引率

0.00%

发文量