In recent years, the use of voice assistants has rapidly grown. Hereby, above all, the user’s speech data is stored and processed on a cloud platform, being the decisive factor for a good performance in speech processing and understanding. Although usually, they can be found in private households, a lot of business cases are also employed using voice assistants for public places, be it as an information service, a tour guide, or a booking system. As long as the systems are used in private spaces, it could be argued that the usage is voluntary and that the user itself is responsible for what is processed by the voice assistant system. When leaving the private space, the voluntary use is not the case anymore, as users may be made aware that their voice is processed in the cloud and background voices can be unintendedly recorded and processed as well. Thus, the usage of voice assistants in public environments raises a lot of privacy concerns. In this contribution, we discuss possible anonymization solutions to hide the speakers’ identity, thus allowing a safe cloud processing of speech data. Thereby, we promote the public use of voice assistants.

The LAST MINUTE corpus comprises multimodal recordings (e.g. video, audio, transcripts) from WOZ interactions in a mundane planning task (Rösner et al., 2011). It is one of the largest corpora with naturalistic data currently available. In this paper we report about first results from attempts to automatically and manually analyze the different modes with respect to emotions and affects exhibited by the subjects. We describe and discuss difficulties encountered due to the strong contrast between the naturalistic recordings and traditional databases with acted emotions.