Subband Analysis for Performance Improvement of Replay Attack Detection in Speaker Verification Systems

S. Garg, Shruti Bhilare, Vivek Kanhangad
{"title":"Subband Analysis for Performance Improvement of Replay Attack Detection in Speaker Verification Systems","authors":"S. Garg, Shruti Bhilare, Vivek Kanhangad","doi":"10.1109/ISBA.2019.8778535","DOIUrl":null,"url":null,"abstract":"Automatic speaker verification systems have been widely employed in a variety of commercial applications. However, advancements in the field of speech technology have equipped the attackers with sophisticated techniques for circumventing speaker verification systems. The state-of-the-art countermeasures are fairly successful in detecting speech synthesis and voice conversion attacks. However, the problem of replay attack detection has not received much attention from the researchers. In this study, we perform subband analysis on constant-Q cepstral coefficient (CQCC) and mel-frequency cepstral coefficient (MFCC) features to improve the performance of replay attack detection. We have performed experiments on the ASVspoof 2017 database which consists of 3566 genuine and 15380 replay utterances. Our experimental results suggest that the features extracted from the high frequency band carries significant discriminatory information for replay attack detection. In particular, our approach achieves an improvement of 36.33% over the baseline replay attack detection method in terms of equal error rate.","PeriodicalId":270033,"journal":{"name":"2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBA.2019.8778535","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

Automatic speaker verification systems have been widely employed in a variety of commercial applications. However, advancements in the field of speech technology have equipped the attackers with sophisticated techniques for circumventing speaker verification systems. The state-of-the-art countermeasures are fairly successful in detecting speech synthesis and voice conversion attacks. However, the problem of replay attack detection has not received much attention from the researchers. In this study, we perform subband analysis on constant-Q cepstral coefficient (CQCC) and mel-frequency cepstral coefficient (MFCC) features to improve the performance of replay attack detection. We have performed experiments on the ASVspoof 2017 database which consists of 3566 genuine and 15380 replay utterances. Our experimental results suggest that the features extracted from the high frequency band carries significant discriminatory information for replay attack detection. In particular, our approach achieves an improvement of 36.33% over the baseline replay attack detection method in terms of equal error rate.
说话人验证系统中重放攻击检测性能改进的子带分析
自动说话人验证系统已广泛应用于各种商业应用。然而,语音技术领域的进步使攻击者具备了绕过说话人验证系统的复杂技术。最先进的对策在检测语音合成和语音转换攻击方面相当成功。然而,重放攻击检测问题一直没有受到研究者的重视。在本研究中,我们对恒q倒谱系数(CQCC)和mel-frequency倒谱系数(MFCC)特征进行子带分析,以提高重放攻击检测的性能。我们在ASVspoof 2017数据库上进行了实验,该数据库包含3566个真实话语和15380个重播话语。实验结果表明,提取的高频特征具有显著的判别信息,可用于重放攻击检测。特别是在等错误率方面,我们的方法比基准重放攻击检测方法提高了36.33%。
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:481959085
Book学术官方微信