The necessity of addressee detection arises in multiparty spoken dialogue systems which deal with human-human-computer interaction. In order to cope with this kind of interaction, such a system is supposed to determine whether the user is addressing the system or another human. The present study is focused on multimodal addressee detection and describes three levels of speech and text analysis: acoustical, syntactical, and lexical. We define the connection between different levels of analysis and the classification performance for different categories of speech and determine the dependence of addressee detection performance on speech recognition accuracy. We also compare the obtained results with the results of the original research performed by the authors of the Smart Video Corpus which we use in our computations. Our most effective meta-classifier working with acoustical, syntactical, and lexical features reaches an unweighted average recall equal to 0.917 showing almost a nine percent advantage over the best baseline model, though this baseline classifier additionally uses head orientation data. We also propose a universal meta-model based on acoustical and syntactical analysis, which may theoretically be applied in different domains.

There is an enormous number of potential applications of the system which is capable to recognize human emotions. Such opportunity can be useful in various applications, e.g., improvement of Spoken Dialogue Systems (SDSs) or monitoring agents in call-centers. Depression is another aspect of human beings which is closely related to emotions. The system, that can automatically diagnose patient's depression can be helpful to physicians in order to support their decisions and avoid critical mistakes. Therefore, the Affect and Depression Recognition Sub-Challenges (ASC and DSC correspondingly) of the second combined open Audio/Visual Emotion and Depression recognition Challenge (AVEC 2014) is focused on estimating emotions and depression. This study presents the results of multimodal affect and depression recognition based on four different segmentation methods, using support vector regression. Furthermore, a speaker identification procedure has been introduced in order to build the speaker-specific emotion/depression recognition systems.

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Despite a rapid increase of public interest for electric mobility, several factors still impede Battery Electric Vehicles’ (BEVs) acceptance. These factors include their limited range and inconvenient charging. For mitigating these limitations to users, certain BEV-specific services are required. Therefore, such services provide a reliable range prediction and routing, including charging-stop planning. The basis of these services is a precise and reliable Energy Demand (ED) prediction. For that matter, aggregated fleet-vehicle data combined with map-specific data (e.g., road slope) form an energetic map, which can serve for precise ED predictions. However, data coverage is paramount for these predictions, more specifically regarding gapless energetic maps. This work aims to eliminate the energetic map’s gaps using two Machine Learning (ML) approaches: regression and classification. The proposed ML solution builds upon the synergy between map-information and crowdsourced driving profiles of 4.6 million kilometres of training and test traces. For evaluation, two test-scenarios capture the models’ performance for the analysed problem in two perspectives. First, we evaluate our ML models, followed by the problem-specific energetic evaluation perspective for better interpretability. From the latter, the results indicate energetic map data imputation performs promisingly better when using the regression instead of the classification model.

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