  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.
