Configurable Privacy-Preserving Automatic Speech Recognition
- URL: http://arxiv.org/abs/2104.00766v1
- Date: Thu, 1 Apr 2021 21:03:49 GMT
- Title: Configurable Privacy-Preserving Automatic Speech Recognition
- Authors: Ranya Aloufi, Hamed Haddadi, David Boyle
- Abstract summary: We investigate whether modular automatic speech recognition can improve privacy in voice assistive systems.
We show privacy concerns and the effects of applying various state-of-the-art techniques to each stage of the system.
We argue this presents new opportunities for privacy-preserving applications incorporating ASR.
- Score: 5.730142956540673
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Voice assistive technologies have given rise to far-reaching privacy and
security concerns. In this paper we investigate whether modular automatic
speech recognition (ASR) can improve privacy in voice assistive systems by
combining independently trained separation, recognition, and discretization
modules to design configurable privacy-preserving ASR systems. We evaluate
privacy concerns and the effects of applying various state-of-the-art
techniques at each stage of the system, and report results using task-specific
metrics (i.e. WER, ABX, and accuracy). We show that overlapping speech inputs
to ASR systems present further privacy concerns, and how these may be mitigated
using speech separation and optimization techniques. Our discretization module
is shown to minimize paralinguistics privacy leakage from ASR acoustic models
to levels commensurate with random guessing. We show that voice privacy can be
configurable, and argue this presents new opportunities for privacy-preserving
applications incorporating ASR.
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