BibTex format
@unpublished{Aloufi:2021,
author = {Aloufi, R and Haddadi, H and Boyle, D},
publisher = {arXiv},
title = {Configurable privacy-preserving automatic speech recognition},
url = {http://arxiv.org/abs/2104.00766v1},
year = {2021}
}
Publications
Citation
RIS format (EndNote, RefMan)
TY - UNPB
AB - Voice assistive technologies have given rise to far-reaching privacy andsecurity concerns. In this paper we investigate whether modular automaticspeech recognition (ASR) can improve privacy in voice assistive systems bycombining independently trained separation, recognition, and discretizationmodules to design configurable privacy-preserving ASR systems. We evaluateprivacy concerns and the effects of applying various state-of-the-arttechniques at each stage of the system, and report results using task-specificmetrics (i.e. WER, ABX, and accuracy). We show that overlapping speech inputsto ASR systems present further privacy concerns, and how these may be mitigatedusing speech separation and optimization techniques. Our discretization moduleis shown to minimize paralinguistics privacy leakage from ASR acoustic modelsto levels commensurate with random guessing. We show that voice privacy can beconfigurable, and argue this presents new opportunities for privacy-preservingapplications incorporating ASR.
AU - Aloufi,R
AU - Haddadi,H
AU - Boyle,D
PB - arXiv
PY - 2021///
TI - Configurable privacy-preserving automatic speech recognition
UR - http://arxiv.org/abs/2104.00766v1
UR - http://hdl.handle.net/10044/1/90227
ER -