My Voiceprint Is My Authenticator: A Two-Layer Authentication Approach Using Voiceprint for Voice Assistants

Chang, Yun-Tai; Dupuis, Marc

doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00243

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…Chang [127] proposed a two-layer authentication method using a voiceprint to mitigate replay attacks. Similarly, the VoiceLive system addressed a replay attack using extracts of the TDoA of each phoneme sound to distinguish between a passphrase spoken by a live user and a replayed one.…”

Section: Securitymentioning

confidence: 99%

A Survey of Human-Computer Interaction (HCI) & Natural Habits-based Behavioral Biometric Modalities for User Recognition Schemes

Gupta¹,

Maple²,

Crispo³

et al. 2022

Preprint

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The proliferation of Internet of Things (IoT) systems is having a profound impact across all aspects of life. Recognising and identifying particular users is central to delivering the personalised experience that citizens want to experience, and that organisations wish to deliver. This article presents a survey of human-computer interaction-based (HCI-based) and natural habits-based behavioral biometrics that can be acquired unobtrusively through smart devices or IoT sensors for user recognition purposes. Robust and usable user recognition is also a security requirement for emerging IoT ecosystems to protect them from adversaries. Typically, it can be specified as a fundamental building block for most types of human-to-things accountability principles and access-control methods. However, end-users are facing numerous security and usability challenges in using currently available knowledge- and token-based recognition (i.e., authentication and identification) schemes. To address the limitations of conventional recognition schemes, biometrics, naturally come as a first choice to supporting sophisticated user recognition solutions. We perform a comprehensive review of touch-stroke, swipe, touch signature, hand-movements, voice, gait and footstep behavioral biometrics modalities. This survey analyzes the recent state-of-the-art research of these behavioral biometrics with a goal to identify their attributes and features for generating unique identification signatures. Finally, we present security, privacy, and usability evaluations that can strengthen the designing of robust and usable user recognition schemes for IoT applications.

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Section: Securitymentioning

confidence: 99%

A Survey of Human-Computer Interaction (HCI) & Natural Habits-based Behavioral Biometric Modalities for User Recognition Schemes

Gupta¹,

Maple²,

Crispo³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This is analogous to two-factor authentication, in that the phrase identification (PID) and the speaker identification (SID), both, have to match for the user to gain access. The applications of TD-ASV include, but are not limited to, biometric verification in healthcare [1], banking, forensics [2], and privacy protection in personalized voice-assistants [3].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Use of an Unsupervised Autoregressive Model as a Shared Encoder for Text-Dependent Speaker Verification

Ravi¹,

Fan²,

Afshan³

et al. 2020

Preprint

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In this paper, we propose a novel way of addressing textdependent automatic speaker verification (TD-ASV) by using a shared-encoder with task-specific decoders. An autoregressive predictive coding (APC) encoder is pre-trained in an unsupervised manner using both out-of-domain (LibriSpeech, VoxCeleb) and in-domain (DeepMine) unlabeled datasets to learn generic, high-level feature representation that encapsulates speaker and phonetic content. Two task-specific decoders were trained using labeled datasets to classify speakers (SID) and phrases (PID). Speaker embeddings extracted from the SID decoder were scored using a PLDA. SID and PID systems were fused at the score level. There is a 51.9% relative improvement in minDCF for our system compared to the fully supervised xvector baseline on the cross-lingual DeepMine dataset. However, the i-vector/HMM method outperformed the proposed APC encoder-decoder system. A fusion of the x-vector/PLDA baseline and the SID/PLDA scores prior to PID fusion further improved performance by 15% indicating complementarity of the proposed approach to the x-vector system. We show that the proposed approach can leverage from large, unlabeled, datarich domains, and learn speech patterns independent of downstream tasks. Such a system can provide competitive performance in domain-mismatched scenarios where test data is from data-scarce domains.

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TASA : A Virtual AI Assistant with Multilevel Authentication Using Face Recognition

Pachpute,

Lomte,

Pawar

et al. 2023

2023 7th International Conference on Computing, Communication, Control and Automation (ICCUBEA)

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My Voiceprint Is My Authenticator: A Two-Layer Authentication Approach Using Voiceprint for Voice Assistants

Cited by 8 publications

References 25 publications

A Survey of Human-Computer Interaction (HCI) & Natural Habits-based Behavioral Biometric Modalities for User Recognition Schemes

A Survey of Human-Computer Interaction (HCI) & Natural Habits-based Behavioral Biometric Modalities for User Recognition Schemes

Exploring the Use of an Unsupervised Autoregressive Model as a Shared Encoder for Text-Dependent Speaker Verification

TASA : A Virtual AI Assistant with Multilevel Authentication Using Face Recognition

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