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Title: Privacy-preserving iVector based speaker verification
Authors: Rahulamathavan, Yogachandran
Sutharsini Kunaraj, R.
Indranil Ghosh, Ray
Lu, Rongxing
Rajarajan, Muttukrishnan
Keywords: Privacy
Security
Speech
iVector
Authentication
Random domain
Issue Date: 2018
Publisher: © Institute of Electrical and Electronics Engineers
Citation: RATHULAMATHAVAN, Y. ... et al., 2018. Privacy-preserving iVector based speaker verification. IEEE/ACM Transactions on Audio, Speech and Language Processing, Doi: 10.1109/TASLP.2018.2882731
Abstract: This work introduces an efficient algorithm to develop a privacy-preserving (PP) voice verification based on iVector and linear discriminant analysis techniques. This research considers a scenario in which users enrol their voice biometric to access different services (i.e., banking). Once enrolment is completed, users can verify themselves using their voice-print instead of alphanumeric passwords. Since a voice-print is unique for everyone, storing it with a third-party server raises several privacy concerns. To address this challenge, this work proposes a novel technique based on randomisation to carry out voice authentication, which allows the user to enrol and verify their voice in the randomised domain. To achieve this, the iVector based voice verification technique has been redesigned to work on the randomised domain. The proposed algorithm is validated using a well known speech dataset. The proposed algorithm neither compromises the authentication accuracy nor adds additional complexity due to the randomisation operations.
Description: Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor: The work was supported by the EU Horizon2020 programme under EU Grant H2020-EU.3.7 (Project ID: 653586), NSERC Discovery Grants (04009) and LMCRF-S-2018-03.
Version: Accepted for publication
DOI: 10.1109/TASLP.2018.2882731
URI: https://dspace.lboro.ac.uk/2134/36321
Publisher Link: http://doi.org/10.1109/TASLP.2018.2882731
ISSN: 2329-9290
Appears in Collections:Published Articles (Loughborough University London)

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