Text independent automatic speaker recognition using selforganizing maps

A.T. Mafra, M. Simões
{"title":"Text independent automatic speaker recognition using selforganizing maps","authors":"A.T. Mafra, M. Simões","doi":"10.1109/IAS.2004.1348670","DOIUrl":null,"url":null,"abstract":"This work presents one implementation of an automatic speaker recognition system, based on selforganizing map (SOM) neural networks. The voice of each speaker is modeled by a SOM, trained to specialize in the quantization of feature vectors (MFCCs) extracted from his voice. When a test sample is presented, it is quantized by all SpMs, that compete for the speaker: the SOM with smallest quantization error defines the speaker. The system was tested on a speaker identification task over a 14 speaker set, with phrases from three phonetically balanced sets and one variable answer set. The results comprovate the method's efficiency.","PeriodicalId":131410,"journal":{"name":"Conference Record of the 2004 IEEE Industry Applications Conference, 2004. 39th IAS Annual Meeting.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Record of the 2004 IEEE Industry Applications Conference, 2004. 39th IAS Annual Meeting.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAS.2004.1348670","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

Abstract

This work presents one implementation of an automatic speaker recognition system, based on selforganizing map (SOM) neural networks. The voice of each speaker is modeled by a SOM, trained to specialize in the quantization of feature vectors (MFCCs) extracted from his voice. When a test sample is presented, it is quantized by all SpMs, that compete for the speaker: the SOM with smallest quantization error defines the speaker. The system was tested on a speaker identification task over a 14 speaker set, with phrases from three phonetically balanced sets and one variable answer set. The results comprovate the method's efficiency.
使用自组织地图的文本独立自动说话人识别
本文提出了一种基于自组织映射(SOM)神经网络的自动说话人识别系统。每个说话人的声音都由SOM建模,SOM专门训练从说话人的声音中提取的特征向量(mfccc)的量化。当给出一个测试样本时,它被所有争夺扬声器的spm量化:量化误差最小的SOM定义扬声器。该系统在一个14人的说话人识别任务中进行了测试,其中包括来自三个语音平衡集和一个变量答案集的短语。结果证明了该方法的有效性。
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