  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.
