Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?

Abstract

Automatic speech recognition (ASR) is a key technology in many services and applications. This typically requires user devices to send their speech data to the cloud for ASR decoding. As the speech signal carries a lot of information about the speaker, this raises serious privacy concerns. As a solution, an encoder may reside on each user device which performs local computations to anonymize the representation. In this paper, we focus on the protection of speaker identity and study the extent to which users can be recognized based on the encoded representation of their speech as obtained by a deep encoder-decoder architecture trained for ASR. Through speaker identification and verification experiments on the Librispeech corpus with open and closed sets of speakers, we show that the representations obtained from a standard architecture still carry a lot of information about speaker identity. We then propose to use adversarial training to learn representations that perform well in ASR while hiding speaker identity. Our results demonstrate that adversarial training dramatically reduces the closed-set classification accuracy, but this does not translate into increased open-set verification error hence into increased protection of the speaker identity in practice. We suggest several possible reasons behind this negative result.

References in corpus (6)

• Attention-Based Models for Speech Recognition
• Speaker-Invariant Training via Adversarial Learning
• Fully Convolutional Speech Recognition
• Invariant Representations for Noisy Speech Recognition
• The Insecurity of Home Digital Voice Assistants -- Amazon Alexa as a Case Study
• Learning Anonymized Representations with Adversarial Neural Networks

Cited by in corpus (11)

• Introducing the VoicePrivacy Initiative
• Preech: A System for Privacy-Preserving Speech Transcription
• Adversarial representation learning for private speech generation
• Adversarial Representation Learning for Robust Privacy Preservation in Audio
• Representation Learning for Audio Privacy Preservation using Source Separation and Robust Adversarial Learning
• Voice-Indistinguishability: Protecting Voiceprint in Privacy-Preserving Speech Data Release
• Speaker De-identification System using Autoencoders and Adversarial Training
• Privacy-oriented manipulation of speaker representations
• An Attribute-Aligned Strategy for Learning Speech Representation
• Benchmarking and challenges in security and privacy for voice biometrics
• A Tandem Framework Balancing Privacy and Security for Voice User Interfaces