Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?
This addresses privacy concerns for users of cloud-based ASR services, but the results are incremental as they highlight limitations in current adversarial methods.
The paper tackled the problem of protecting speaker identity in automatic speech recognition (ASR) by using adversarial training to anonymize speech representations, but found that while it reduced closed-set classification accuracy, it did not increase open-set verification error, failing to provide practical privacy protection.
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.