Privacy and security are major concerns when sharing and collecting speech data for cloud services such as automatic speech recognition (ASR) and speech emotion recognition (SER). Existing solutions for client-side privacy mainly focus on voice conversion or voice modification to convert a raw utterance into another one with similar content but different, or no, identity-related information. However, an alternative approach to share speech data under the form of privacy-preserving representations has been largely under-explored. To fill this gap, we propose a speech anonymization framework that provides formal privacy guarantees via noise perturbation to a selected subset of the high-utility representations extracted using a pre-trained speech encoder. The subset is chosen with a Transformer-based privacy-risk saliency estimator. We validate our framework on four tasks, namely, Automatic Speaker Verification (ASV), ASR, SER and Intent Classification (IC) for privacy and utility assessment. Experimental results show that our approach is able to achieve a competitive, or even better, utility compared to the baselines that use voice conversion and voice modification, providing the same level of privacy. Moreover, the easily-controlled amount of perturbation allows our framework to have a flexible range of privacy-utility trade-offs without re-training any components.
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