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arXiv · 1909.04198

Preech: A System for Privacy-Preserving Speech Transcription

Shimaa Ahmed·Amrita Roy Chowdhury·Kassem Fawaz·Parmesh Ramanathan
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Abstract

New Advances in machine learning have made Automated Speech Recognition (ASR) systems practical and more scalable. These systems, however, pose serious privacy threats as speech is a rich source of sensitive acoustic and textual information. Although offline and open-source ASR eliminates the privacy risks, its transcription performance is inferior to that of cloud-based ASR systems, especially for real-world use cases. In this paper, we propose Pr$\epsilon\epsilon$ch, an end-to-end speech transcription system which lies at an intermediate point in the privacy-utility spectrum. It protects the acoustic features of the speakers' voices and protects the privacy of the textual content at an improved performance relative to offline ASR. Additionally, Pr$\epsilon\epsilon$ch provides several control knobs to allow customizable utility-usability-privacy trade-off. It relies on cloud-based services to transcribe a speech file after applying a series of privacy-preserving operations on the user's side. We perform a comprehensive evaluation of Pr$\epsilon\epsilon$ch, using diverse real-world datasets, that demonstrates its effectiveness. Pr$\epsilon\epsilon$ch provides transcriptions at a 2% to 32.25% (mean 17.34%) relative improvement in word error rate over Deep Speech, while fully obfuscating the speakers' voice biometrics and allowing only a differentially private view of the textual content.

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Shimaa Ahmed, Amrita Roy Chowdhury, Kassem Fawaz, Parmesh Ramanathan. 2019-09-09. Preech: A System for Privacy-Preserving Speech Transcription. https://arxiv.org/abs/1909.04198

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