Speech-driven user interfaces are becoming more common in our lives. To interact with such systems naturally and effectively, machines need to recognize the emotional states of users and respond to them accordingly. At the heart of the emotion recognition research done to this end lies the emotion representation that enables machines to learn and predict emotions. Speech emotion recognition studies use a wide range of low-to-high-level acoustic features for representation purposes such as LLDs, their functionals, and BoAW. In this paper, we present a new method for extracting a novel set of high-level features for classifying emotions. For this purpose, we (1) reduce the dimension of discrete-time speech signals, (2) perform a quantization operation on the new signals and assign a distinct symbol to each quantization level, (3) use the symbol sequences representing the signals to extract discriminative patterns that are capable of distinguishing different emotions from each other, and (4) generate a separate set of features for each emotion from the extracted patterns. Experimental results show that pattern features outperform Energy, Voicing, MFCC, Spectral, and RASTA feature sets. We also demonstrate that combining the pattern-based features and the acoustic features further improves the classification performance.

In this paper, we investigate the link between the personality traits and group performance in terms of the verbal content. We further study the variability in the verbal interaction between different performance groups. Towards this goal, we extract topics representing the content of meetings as well as term-frequencies of items that play a critical role in the decision task. We use a dataset where each group performs the winter survival task, in which the task is to decide on the ranking of different items with respect to the importance of each item for their survival. In the experiments, we contrast the ranking of items with respect to their term frequencies and compare the differences between topics both for distinct personality traits and group performances. Results of the term-frequency based approach show that influential people put correct emphasis on items more than dominant people. The topic-based method reveals that influential people consider the majority of items by providing usage instructions for alternative scenarios and that dominant people focus only on a small subset of items by stressing their significance. High-performance groups assess items in a similar manner to influential and dominant people, i.e. a wide range of items are considered and their importance is explained. Low-performance groups, on the other hand, concentrate on the situation they are in rather than the items and their usages.

This paper addresses the problem of predicting the performance of decision-making groups. Towards this goal, we evaluate the predictive power of group attributes and discussion dynamics by using automatically extracted features, such as group members' aural and visual cues, interaction between team members, and influence of each team member, as well as self-reported features such as personality- and perception-related cues, hierarchical structure of the group, and individual- and group-level task performances. We tackle the inference problem from two angles depending on the way that features are extracted: 1) a holistic approach based on the entire meeting, and 2) a sequential approach based on the thin slices of the meeting. In the former, key factors affecting the group performance are identified and the prediction is achieved by support vector machines. As for the latter, we compare and contrast the classification performance of an influence model-based novel classifier with that of hidden Markov model (HMM). Experimental results indicate that the group looking cues and the influence cues are major predictors of group performance and the influence model outperforms the HMM in almost all experimental conditions. We also show that combining classifiers covering unique aspects of data results in improvement in the classification performance.

Activity recognition is a key task for the development of advanced and effective ubiquitous applications in fields like ambient assisted living. A major problem in designing effective recognition algorithms is the difficulty of incorporating long-range dependencies between distant time instants without incurring substantial increase in computational complexity of inference. In this paper we present a novel approach for introducing long-range interactions based on sequential pattern mining. The algorithm searches for patterns characterizing time segments during which the same activity is performed. A probabilistic model is learned to represent the distribution of pattern matches along sequences, trying to maximize the coverage of an activity segment by a pattern match. The model is integrated in a segmental labeling algorithm and applied to novel sequences, tagged according to matches of the extracted patterns. The rationale of the approach is that restricting dependencies to span the same activity segment (i.e., sharing the same label), allows keeping inference tractable. An experimental evaluation shows that enriching sensor-based representations with the mined patterns allows improving results over sequential and segmental labeling algorithms in most of the cases. An analysis of the discovered patterns highlights non-trivial interactions spanning over a significant time horizon.