  The emergence of voice-assistant devices ushers in delightful user
experiences not just on the smart home front, but also in diverse educational
environments from classrooms to personalized-learning/tutoring. However, the
use of voice as an interaction modality also could result in exposure of user's
identity, and hinders the broader adoption of voice interfaces; this is
especially important in environments where children are present and their voice
privacy needs to be protected. To this end, building on state-of-the-art
techniques proposed in the literature, we design and evaluate a practical and
efficient framework for voice privacy at the source. The approach combines
speaker identification (SID) and speech conversion methods to randomly disguise
the identity of users right on the device that records the speech, while
ensuring that the transformed utterances of users can still be successfully
transcribed by Automatic Speech Recognition (ASR) solutions. We evaluate the
ASR performance of the conversion in terms of word error rate and show the
promise of this framework in preserving the content of the input speech.
