  Voice controlled devices and services have become very popular in the
consumer IoT. Cloud-based speech analysis services extract information from
voice inputs using speech recognition techniques. Services providers can thus
build very accurate profiles of users' demographic categories, personal
preferences, emotional states, etc., and may therefore significantly compromise
their privacy. To address this problem, we have developed a privacy-preserving
intermediate layer between users and cloud services to sanitize voice input
directly at edge devices. We use CycleGAN-based speech conversion to remove
sensitive information from raw voice input signals before regenerating
neutralized signals for forwarding. We implement and evaluate our emotion
filtering approach using a relatively cheap Raspberry Pi 4, and show that
performance accuracy is not compromised at the edge. In fact, signals generated
at the edge differ only slightly (~0.16%) from cloud-based approaches for
speech recognition. Experimental evaluation of generated signals show that
identification of the emotional state of a speaker can be reduced by ~91%.
