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Found 8 papers, 5 papers with code
The VoicePrivacy 2024 Challenge Evaluation Plan
The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional states.
Speaker anonymization using neural audio codec language models
The vast majority of approaches to speaker anonymization involve the extraction of fundamental frequency estimates, linguistic features and a speaker embedding which is perturbed to obfuscate the speaker identity before an anonymized speech waveform is resynthesized using a vocoder.
Fairness and Privacy in Voice Biometrics:A Study of Gender Influences Using wav2vec 2.0
This study investigates the impact of gender information on utility, privacy, and fairness in voice biometric systems, guided by the General Data Protection Regulation (GDPR) mandates, which underscore the need for minimizing the processing and storage of private and sensitive data, and ensuring fairness in automated decision-making systems.
Vocoder drift compensation by x-vector alignment in speaker anonymisation
For the most popular x-vector-based approaches to speaker anonymisation, the bulk of the anonymisation can stem from vocoding rather than from the core anonymisation function which is used to substitute an original speaker x-vector with that of a fictitious pseudo-speaker.
Differentially Private Adversarial Auto-Encoder to Protect Gender in Voice Biometrics
In this paper, we present an adversarial Auto-Encoder--based approach to hide gender-related information in speaker embeddings, while preserving their effectiveness for speaker verification.
Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systems
We present Malafide, a universal adversarial attack against automatic speaker verification (ASV) spoofing countermeasures (CMs).
Vocoder drift in x-vector-based speaker anonymization
State-of-the-art approaches to speaker anonymization typically employ some form of perturbation function to conceal speaker information contained within an x-vector embedding, then resynthesize utterances in the voice of a new pseudo-speaker using a vocoder.
Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection
This paper reports the first successful application of a differentiable architecture search (DARTS) approach to the deepfake and spoofing detection problems.
