University of Hertfordshire

By the same authors

Fraud Detection in Voice-Based Identity Authentication Applications and Services

Research output: Chapter in Book/Report/Conference proceedingConference contribution

View graph of relations
Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
PublisherIEEE Computer Society
Pages1074-1081
Number of pages8
ISBN (Electronic)9781509054725
DOIs
Publication statusPublished - 30 Jan 2017
Event16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain
Duration: 12 Dec 201615 Dec 2016

Conference

Conference16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
CountrySpain
CityBarcelona
Period12/12/1615/12/16

Abstract

Keeping track of the multiple passwords, PINs, memorable dates and other authentication details needed to gainremote access to accounts is one of modern life's less appealingchallenges. The employment of a voice-based verification as abiometric technology for both children and adults could be agood replacement to the old fashioned memory dependentprocedure. Using voice for authentication could be beneficial inseveral application areas, including, security, protection, education, call-based and web-based services. Voice-basedbiometric applications are subject to different types of spoofingattacks. The most accessible and affordable type of spoofing for avoice-based biometrics system is a replay attack. Replay, which isto playback a pre-recorded speech sample, presents a genuinerisk to automatic speaker verification technology. This workpresents two architectures for detecting frauds caused by replayattacks in a voice-based biometrics authentication systems. Experimental results confirmed that obtained performancesfrom both methods could further improve by applying a machinelearning algorithm for performing fusion at the score level. Theperformance of both methods further improved by fusion usingindependent sources of scores in different architectures.

ID: 11448936