Speaker De-identification System Using Autoencoders And Adversarial Training

Abstract

The fast increase of web services and mobile apps, which collect personal data from users, increases the risk that their privacy may be severely compromised. In particular, the increasing variety of spoken language interfaces and voice assistants empowered by the vertiginous breakthroughs in Deep Learning are prompting important concerns in the European Union to preserve speech data privacy. For instance, an attacker can record speech from users and impersonate them to get access to systems requiring voice identification. Hacking speaker profiles from users is also possible by means of existing technology to extract speaker, linguistic (e.g., dialect) and paralinguistic features (e.g., age) from the speech signal. In order to mitigate these weaknesses, in this paper, we propose a speaker de-identification system based on adversarial training and autoencoders in order to suppress speaker, gender, and accent information from speech. Experimental results show that combining adversarial le

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyespinozacuadros2020speaker

Related papers

• Privacy-utility Balanced Voice De-identification Using Adversarial Examples (2022)0.00
• Adversarial Attack And Defense Strategies For Deep Speaker Recognition Systems (2020)13.39
• Adversarial Disentanglement Of Speaker Representation For Attribute-driven Privacy Preservation (2020)9.59
• Ghostvec: A New Threat To Speaker Privacy Of End-to-end Speech Recognition System (2023)0.00
• Adversarial Defense For Deep Speaker Recognition Using Hybrid Adversarial Training (2020)9.59
• Asynchronous Voice Anonymization Using Adversarial Perturbation On Speaker Embedding (2024)7.16
• Attacking Voice Anonymization Systems With Augmented Feature And Speaker Identity Difference (2024)6.34
• On-device Speaker Anonymization Of Acoustic Embeddings For ASR Based Onflexible Location Gradient Reversal Layer (2023)0.00