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.

翻译：智能音箱语音助手（如Amazon Echo和Google Home）因其与智能家居设备及物联网技术的无缝集成而得到广泛应用。这些语音助手服务引发了隐私担忧，尤其是它们能够访问用户的语音。本文聚焦于一种具体用例：通过语音情感识别对用户情绪进行未经授权和无法追责的监控。本文提出DARE-GP解决方案，通过生成加性噪声来掩蔽用户的情感信息，同时保留语音中与文本转录相关的部分。该方法采用受限遗传编程方法，学习表征目标用户情感内容的频谱特征，进而生成通用对抗性音频扰动以实现隐私保护。与现有研究不同，DARE-GP具备以下特性：a) 对未听过语音进行实时保护，b) 抵御未知黑盒语音情感识别分类器，c) 保护语音转录内容，d) 在真实声学环境中实现上述功能。此外，该规避方法对知情攻击者采用的防御策略具有鲁棒性。本文最终通过声学评估，在集成唤醒词系统的小型计算设备（树莓派）上对两款市售商用智能音箱进行测试，验证了其在实际场景中实时部署的有效性。