  Smart speaker voice assistants (VAs) such as Amazon Echo and Google Home have
been widely adopted due to their seamless integration with smart home devices
and the Internet of Things (IoT) technologies. These VA services raise privacy
concerns, especially due to their access to our speech. This work considers one
such use case: the unaccountable and unauthorized surveillance of a user's
emotion via speech emotion recognition (SER). This paper presents DARE-GP, a
solution that creates additive noise to mask users' emotional information while
preserving the transcription-relevant portions of their speech. DARE-GP does
this by using a constrained genetic programming approach to learn the spectral
frequency traits that depict target users' emotional content, and then
generating a universal adversarial audio perturbation that provides this
privacy protection. Unlike existing works, DARE-GP provides: a) real-time
protection of previously unheard utterances, b) against previously unseen
black-box SER classifiers, c) while protecting speech transcription, and d)
does so in a realistic, acoustic environment. Further, this evasion is robust
against defenses employed by a knowledgeable adversary. The evaluations in this
work culminate with acoustic evaluations against two off-the-shelf commercial
smart speakers using a small-form-factor (raspberry pi) integrated with a
wake-word system to evaluate the efficacy of its real-world, real-time
deployment.
