Introducing the VoicePrivacy Initiative

Abstract

The VoicePrivacy initiative aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the evaluation methodology, and benchmarking solutions through a series of challenges. In this paper, we formulate the voice anonymization task selected for the VoicePrivacy 2020 Challenge and describe the datasets used for system development and evaluation. We also present the attack models and the associated objective and subjective evaluation metrics. We introduce two anonymization baselines and report objective evaluation results.

arXiv commentInterspeech 2020

References in corpus (8)

• VoxCeleb2: Deep Speaker Recognition
• Inverting Gradients -- How easy is it to break privacy in federated learning?
• Evaluating Voice Conversion-based Privacy Protection against Informed Attackers
• The GDPR & Speech Data: Reflections of Legal and Technology Communities, First Steps towards a Common Understanding
• Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?
• VoiceMask: Anonymize and Sanitize Voice Input on Mobile Devices
• Neural Harmonic-plus-Noise Waveform Model with Trainable Maximum Voice Frequency for Text-to-Speech Synthesis
• Voice-Indistinguishability: Protecting Voiceprint in Privacy-Preserving Speech Data Release

Cited by in corpus (6)

• The VoicePrivacy 2020 Challenge: Results and findings
• The Privacy ZEBRA: Zero Evidence Biometric Recognition Assessment
• Privacy attacks for automatic speech recognition acoustic models in a federated learning framework
• Speaker Anonymization with Distribution-Preserving X-Vector Generation for the VoicePrivacy Challenge 2020
• Understanding the Tradeoffs in Client-side Privacy for Downstream Speech Tasks
• A Tandem Framework Balancing Privacy and Security for Voice User Interfaces