Using Complexity-Identical Human- and Machine-Directed Utterances to Investigate Addressee Detection for Spoken Dialogue Systems.

Human-machine addressee detection (H-M AD) is a modern paralinguistics and dialogue challenge that arises in multiparty conversations between several people and a spoken dialogue system (SDS) since the users may also talk to each other and even to themselves while interacting with the system. The SDS is supposed to determine whether it is being addressed or not. All existing studies on acoustic H-M AD were conducted on corpora designed in such a way that a human addressee and a machine played different dialogue roles.

EmoTour: Estimating Emotion and Satisfaction of Users Based on Behavioral Cues and Audiovisual Data.

With the spread of smart devices, people may obtain a variety of information on their surrounding environment thanks to sensing technologies. To design more context-aware systems, psychological user context (e.g.

The next step: intelligent digital assistance for clinical operating rooms.

With the emergence of new technologies, the surgical working environment becomes increasingly complex and comprises many medical devices that have to be taken cared of. However, the goal is to reduce the workload of the surgical team to allow them to fully focus on the actual surgical procedure. Therefore, new strategies are needed to keep the working environment manageable.