Who Do I Sound like? Showcasing Speaker Recognition Technology by Youtube Voice Search

Vestman, Ville; Soomro, Bilal; Kanervisto, Anssi; Hautamäki, Ville; Kinnunen, Tomi

doi:10.1109/icassp.2019.8683272

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…In our model, the attacker uses a readily-available public-domain ASV system to rank and select potential target speakers, but without any further numerical feedback or system optimization. Such 'passive' ASV system could be, for instance, a voice search service that finds most similar speakers to the user's voice from a public video archive -see [36,37] as examples.…”

mentioning

confidence: 99%

Voice Mimicry Attacks Assisted by Automatic Speaker Verification

Vestman

Kinnunen

Hautamäki

et al. 2020

Computer Speech & Language

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In this work, we simulate a scenario, where a publicly available ASV system is used to enhance mimicry attacks against another closed source ASV system. In specific, ASV technology is used to perform a similarity search between the voices of recruited attackers (6) and potential target speakers (7,365) from VoxCeleb corpora to find the closest targets for each of the attackers. In addition, we consider 'median', 'furthest', and 'common' targets to serve as a reference points.Our goal is to gain insights how well similarity rankings transfer from the attacker's ASV system to the attacked ASV system, whether the attackers are able to improve their attacks by mimicking, and how the properties of the voices of attackers change due to mimicking. We address these questions through ASV experiments, listening tests, and prosodic and formant analyses. For the ASV experiments, we use i-vector technology in the attacker side, and x-vectors in the attacked side. For the listening tests, we recruit listeners through crowdsourcing.The results of the ASV experiments indicate that the speaker similarity scores transfer well from one ASV system to another. Both the ASV experiments and the listening tests reveal that the mimicry attempts do not, in general, help in bringing attacker's scores closer to the target's. A detailed analysis shows that mimicking does not improve attacks, when the natural voices of attackers and targets are similar to each other. The analysis of prosody and formants suggests that the attackers were able to considerably change their speaking rates when mimicking, but the changes in F0 and formants were modest. Overall, the results suggest that untrained impersonators do not pose a high threat towards ASV systems, but the use of ASV systems to attack other ASV systems is a potential threat.

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confidence: 99%

Voice Mimicry Attacks Assisted by Automatic Speaker Verification

Vestman

Kinnunen

Hautamäki

et al. 2020

Computer Speech & Language

Self Cite

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“…Specially, generated utterances could be used to attack an automatic speaker verification (ASV) system [2,3,4]. It is also possible to search for information about a person on the Internet by using an ASV system [5]. How to suppress the identity information of a speaker and mitigate the risks is becoming an urgent problem.…”

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confidence: 99%

Speaker Anonymization Using X-vector and Neural Waveform Models

Fang¹,

Wang²,

Yamagishi³

et al. 2019

10th ISCA Workshop on Speech Synthesis (SSW 10)

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The social media revolution has produced a plethora of web services to which users can easily upload and share multimedia documents. Despite the popularity and convenience of such services, the sharing of such inherently personal data, including speech data, raises obvious security and privacy concerns. In particular, a user's speech data may be acquired and used with speech synthesis systems to produce high-quality speech utterances which reflect the same user's speaker identity. These utterances may then be used to attack speaker verification systems. One solution to mitigate these concerns involves the concealing of speaker identities before the sharing of speech data. For this purpose, we present a new approach to speaker anonymization. The idea is to extract linguistic and speaker identity features from an utterance and then to use these with neural acoustic and waveform models to synthesize anonymized speech. The original speaker identity, in the form of timbre, is suppressed and replaced with that of an anonymous pseudo identity. The approach exploits state-of-the-art x-vector speaker representations. These are used to derive anonymized pseudo speaker identities through the combination of multiple, random speaker x-vectors. Experimental results show that the proposed approach is effective in concealing speaker identities. It increases the equal error rate of a speaker verification system while maintaining high quality, anonymized speech.

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Vulnerability issues in Automatic Speaker Verification (ASV) systems

Gupta,

Patil,

Guido

2024

J AUDIO SPEECH MUSIC PROC.

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Claimed identities of speakers can be verified by means of automatic speaker verification (ASV) systems, also known as voice biometric systems. Focusing on security and robustness against spoofing attacks on ASV systems, and observing that the investigation of attacker’s perspectives is capable of leading the way to prevent known and unknown threats to ASV systems, several countermeasures (CMs) have been proposed during ASVspoof 2015, 2017, 2019, and 2021 challenge campaigns that were organized during INTERSPEECH conferences. Furthermore, there is a recent initiative to organize the ASVSpoof 5 challenge with the objective of collecting the massive spoofing/deepfake attack data (i.e., phase 1), and the design of a spoofing-aware ASV system using a single classifier for both ASV and CM, to design integrated CM-ASV solutions (phase 2). To that effect, this paper presents a survey on a diversity of possible strategies and vulnerabilities explored to successfully attack an ASV system, such as target selection, unavailability of global countermeasures to reduce the attacker’s chance to explore the weaknesses, state-of-the-art adversarial attacks based on machine learning, and deepfake generation. This paper also covers the possibility of attacks, such as hardware attacks on ASV systems. Finally, we also discuss the several technological challenges from the attacker’s perspective, which can be exploited to come up with better defence mechanisms for the security of ASV systems.

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Who Do I Sound like? Showcasing Speaker Recognition Technology by Youtube Voice Search

Cited by 5 publications

References 13 publications

Voice Mimicry Attacks Assisted by Automatic Speaker Verification

Voice Mimicry Attacks Assisted by Automatic Speaker Verification

Speaker Anonymization Using X-vector and Neural Waveform Models

Vulnerability issues in Automatic Speaker Verification (ASV) systems

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