V-Cloak: Intelligibility-, Naturalness- Timbre-Preserving Real-Time Voice Anonymization

by   Jiangyi Deng, et al.

Voice data generated on instant messaging or social media applications contains unique user voiceprints that may be abused by malicious adversaries for identity inference or identity theft. Existing voice anonymization techniques, e.g., signal processing and voice conversion/synthesis, suffer from degradation of perceptual quality. In this paper, we develop a voice anonymization system, named V-Cloak, which attains real-time voice anonymization while preserving the intelligibility, naturalness and timbre of the audio. Our designed anonymizer features a one-shot generative model that modulates the features of the original audio at different frequency levels. We train the anonymizer with a carefully-designed loss function. Apart from the anonymity loss, we further incorporate the intelligibility loss and the psychoacoustics-based naturalness loss. The anonymizer can realize untargeted and targeted anonymization to achieve the anonymity goals of unidentifiability and unlinkability. We have conducted extensive experiments on four datasets, i.e., LibriSpeech (English), AISHELL (Chinese), CommonVoice (French) and CommonVoice (Italian), five Automatic Speaker Verification (ASV) systems (including two DNN-based, two statistical and one commercial ASV), and eleven Automatic Speech Recognition (ASR) systems (for different languages). Experiment results confirm that V-Cloak outperforms five baselines in terms of anonymity performance. We also demonstrate that V-Cloak trained only on the VoxCeleb1 dataset against ECAPA-TDNN ASV and DeepSpeech2 ASR has transferable anonymity against other ASVs and cross-language intelligibility for other ASRs. Furthermore, we verify the robustness of V-Cloak against various de-noising techniques and adaptive attacks. Hopefully, V-Cloak may provide a cloak for us in a prism world.


page 1

page 2

page 3

page 4


V2S attack: building DNN-based voice conversion from automatic speaker verification

This paper presents a new voice impersonation attack using voice convers...

Blackbox Untargeted Adversarial Testing of Automatic Speech Recognition Systems

Automatic speech recognition (ASR) systems are prevalent, particularly i...

Unsupervised Cross-Domain Singing Voice Conversion

We present a wav-to-wav generative model for the task of singing voice c...

TTS Skins: Speaker Conversion via ASR

We present a fully convolutional wav-to-wav network for converting betwe...

High Fidelity Speech Regeneration with Application to Speech Enhancement

Speech enhancement has seen great improvement in recent years mainly thr...

Statistical Models in Forensic Voice Comparison

This chapter describes a number of signal-processing and statistical-mod...

Privacy-preserving Similarity Calculation of Speaker Features Using Fully Homomorphic Encryption

Recent advances in machine learning techniques are enabling Automated Sp...

Please sign up or login with your details

Forgot password? Click here to reset