Theophany: Multimodal Speech Augmentation in Instantaneous Privacy Channels

Many factors affect speech intelligibility in face-to-face conversations. These factors lead conversation participants to speak louder and more distinctively, exposing the content to potential eavesdroppers. To address these issues, we introduce Theophany, a privacy-preserving framework for augmenting speech. Theophany establishes ad-hoc social networks between conversation participants to exchange contextual information, improving speech intelligibility in real-time. At the core of Theophany, we develop the first privacy perception model that assesses the privacy risk of a face-to-face conversation based on its topic, location, and participants. This framework allows to develop any privacy-preserving application for face-to-face conversation. We implement the framework within a prototype system that augments the speaker's speech with real-life subtitles to overcome the loss of contextual cues brought by mask-wearing and social distancing during the COVID-19 pandemic. We evaluate Theophany through a user survey and a user study on 53 and 17 participants, respectively. Theophany's privacy predictions match the participants' privacy preferences with an accuracy of 71.26%. Users considered Theophany to be useful to protect their privacy (3.88/5), easy to use (4.71/5), and enjoyable to use (4.24/5). We also raise the question of demographic and individual differences in the design of privacy-preserving solutions.