Generating gender-ambiguous voices for privacy-preserving speech recognition

• URL: http://arxiv.org/abs/2207.01052v1
• Date: Sun, 3 Jul 2022 14:23:02 GMT
• Title: Generating gender-ambiguous voices for privacy-preserving speech recognition
• Authors: Dimitrios Stoidis and Andrea Cavallaro
• Abstract summary: We present a generative adversarial network, GenGAN, that synthesises voices that conceal the gender or identity of a speaker. We condition the generator only on gender information and use an adversarial loss between signal distortion and privacy preservation.
• Score: 38.733077459065704
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Our voice encodes a uniquely identifiable pattern which can be used to infer private attributes, such as gender or identity, that an individual might wish not to reveal when using a speech recognition service. To prevent attribute inference attacks alongside speech recognition tasks, we present a generative adversarial network, GenGAN, that synthesises voices that conceal the gender or identity of a speaker. The proposed network includes a generator with a U-Net architecture that learns to fool a discriminator. We condition the generator only on gender information and use an adversarial loss between signal distortion and privacy preservation. We show that GenGAN improves the trade-off between privacy and utility compared to privacy-preserving representation learning methods that consider gender information as a sensitive attribute to protect.

Related papers

• On the Generation and Removal of Speaker Adversarial Perturbation for Voice-Privacy Protection [45.49915832081347]
  Recent development in voice-privacy protection has shown the positive use cases of the same technique to conceal speaker's voice attribute. This paper examines the reversibility property where an entity generating adversarial perturbations is authorized to remove them and restore original speech. A similar technique could also be used by an investigator to deanonymize a voice-protected speech to restore criminals' identities in security and forensic analysis.
  arXiv  Detail & Related papers  (2024-12-12T11:46:07Z)
• Asynchronous Voice Anonymization Using Adversarial Perturbation On Speaker Embedding [46.25816642820348]
  We focus on altering the voice attributes against machine recognition while retaining human perception. A speech generation framework incorporating a speaker disentanglement mechanism is employed to generate the anonymized speech. Experiments conducted on the LibriSpeech dataset showed that the speaker attributes were obscured with their human perception preserved for 60.71% of the processed utterances.
  arXiv  Detail & Related papers  (2024-06-12T13:33:24Z)
• Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy [22.84840887071428]
  Speaker anonymization aims for hiding the identity of a speaker by changing the voice in speech recordings. This typically comes with a privacy-utility trade-off between protection of individuals and usability of the data for downstream applications. We propose to tackle this issue by generating speaker embeddings using a generative adversarial network with Wasserstein distance as cost function.
  arXiv  Detail & Related papers  (2022-10-13T13:12:42Z)
• Differentially Private Speaker Anonymization [44.90119821614047]
  Sharing real-world speech utterances is key to the training and deployment of voice-based services. Speaker anonymization aims to remove speaker information from a speech utterance while leaving its linguistic and prosodic attributes intact. We show that disentanglement is indeed not perfect: linguistic and prosodic attributes still contain speaker information.
  arXiv  Detail & Related papers  (2022-02-23T23:20:30Z)
• Protecting gender and identity with disentangled speech representations [49.00162808063399]
  We show that protecting gender information in speech is more effective than modelling speaker-identity information. We present a novel way to encode gender information and disentangle two sensitive biometric identifiers.
  arXiv  Detail & Related papers  (2021-04-22T13:31:41Z)
• Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation [17.344080729609026]
  We introduce the concept of attribute-driven privacy preservation in speaker voice representation. It allows a person to hide one or more personal aspects to a potential malicious interceptor and to the application provider. We propose an adversarial autoencoding method that disentangles in the voice representation a given speaker attribute thus allowing its concealment.
  arXiv  Detail & Related papers  (2020-12-08T14:47:23Z)
• Speaker De-identification System using Autoencoders and Adversarial Training [58.720142291102135]
  We propose a speaker de-identification system based on adversarial training and autoencoders. Experimental results show that combining adversarial learning and autoencoders increase the equal error rate of a speaker verification system.
  arXiv  Detail & Related papers  (2020-11-09T19:22:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.