The process engine for pattern recognition and information fusion tasks, the \emph{pepr framework}, aims to empower the researcher to develop novel solutions in the field of pattern recognition and information fusion tasks in a timely manner, by supporting reuse and combination of well tested and established components in an environment, that eases the wiring of distinct algorithms and description of the control flow through graphical tooling. The framework, not only consisting of the runtime environment, comes with several highly useful components that can be leveraged as a starting point in creating new solutions, as well as a graphical process builder that allows for easy development of pattern recognition processes in a graphical, modeled manner. Additionally, numerous work has been invested in order to keep the entry barrier with regards to extending the framework as low as possible, enabling developers to add additional functionality to the framework in as less time as possible.

The goal of this work is to introduce an architecture to automatically detect the amount of stress in the speech signal close to real time. For this an experimental setup to record speech rich in vocabulary and containing different stress levels is presented. Additionally, an experiment explaining the labeling process with a thorough analysis of the labeled data is presented. Fifteen subjects were asked to play an air controller simulation that gradually induced more stress by becoming more difficult to control. During this game the subjects were asked to answer questions, which were then labeled by a different set of subjects in order to receive a subjective target value for each of the answers. A recurrent neural network was used to measure the amount of stress contained in the utterances after training. The neural network estimated the amount of stress at a frequency of 25 Hz and outperformed the human baseline.

In this paper we present the setup of an extensive Wizard-of-Oz environment used for the data collection and the development of a dialogue system. The envisioned Perception and Interaction Assistant will act as an independent dialogue partner. Passively observing the dialogue between the two human users with respect to a limited domain, the system should take the initiative and get meaningfully involved in the communication process when required by the conversational situation. The data collection described here involves audio and video data. We aim at building a rich multi-media data corpus to be used as a basis for our research which includes, inter alia, speech and gaze direction recognition, dialogue modelling and proactivity of the system. We further aspire to obtain data with emotional content to perfom research on emotion recognition, psychopysiological and usability analysis.