  Speech models are often trained on sensitive data in order to improve model
performance, leading to potential privacy leakage. Our work considers noise
masking attacks, introduced by Amid et al. 2022, which attack automatic speech
recognition (ASR) models by requesting a transcript of an utterance which is
partially replaced with noise. They show that when a record has been seen at
training time, the model will transcribe the noisy record with its memorized
sensitive transcript. In our work, we extend these attacks beyond ASR models,
to attack pretrained speech encoders. Our method fine-tunes the encoder to
produce an ASR model, and then performs noise masking on this model, which we
find recovers private information from the pretraining data, despite the model
never having seen transcripts at pretraining time! We show how to improve the
precision of these attacks and investigate a number of countermeasures to our
attacks.
