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We are interested in understanding human personality and its manifestations in human interactions. The automatic analysis of such personality traits in natural conversation is quite complex due to the user-profiled corpora acquisition, annotation task and multidimensional modeling. While in the experimental psychology research this topic has been addressed(More)
We investigate the clarification strategies exhibited by a hybrid POMDP dialog manager based on data obtained from a phone-based user study. The dialog manager combines task structures with a number of POMDP policies each optimized for obtaining an individual concept. We investigate the relationship between dialog length and task completion. In order to(More)
Six participants of a 105-day experiment in an isolated environment were studied in order to identify subconscious mechanisms of their psychophysiological changes during the experiment. We used the method of neurocognitive diagnostics based on the analysis of the evoked electrocardiographic (EEG) potentials caused by stimuli that were below the conscious(More)
Investigation into the mechanisms of the individual psychophysiological adaptation to life-threatening situations was performed. The data analysis of evoked potentials induced with verbal stimuli in 30-50-msec exposures was executed. Cross-correlation and wavelet analysis, as well as neurone algorithms were used to define total brain response to each(More)
An accurate identification dialog acts (DAs), which represent the illocutionary aspect of communication, is essential to support the understanding of human conversations. This requires 1) the segmentation of human-human dialogs into turns, 2) the intra-turn segmentation into DA boundaries and 3) the classification of each segment according to a DA tag. This(More)
—Conversational systems use deterministic rules that trigger actions such as requests for confirmation or clarification. More recently, Reinforcement Learning and (Partially Observable) Markov Decision Processes have been proposed for this task. In this paper, we investigate action selection strategies for dialogue management, in particular the(More)
We have developed a complete spoken dialogue framework that includes rule-based and trainable dialogue managers, speech recognition, spoken language understanding and generation modules, and a comprehensive web visualization interface. We present a spoken dialogue system based on Reinforcement Learning that goes beyond standard rule based models and(More)
We address several challenges for applying statistical dialog managers based on Partially Observable Markov Models to real world problems: to deal with large numbers of concepts, we use individual POMDP policies for each concept. To control the use of the concept policies, the dialog manager uses explicit task structures. The POMDP policies model the(More)
Automatic emotion recognition from speech is limited by the ability to discover the relevant predicting features. The common approach is to extract a very large set of features over a generally long analysis time window. In this paper we investigate the applicability of two-sample Kolmogorov-Smirnov statistical test (KST) to the problem of segmental speech(More)
We investigate the separatrix splitting of the double mathematical pendulum. The numerical method of nding periodic hyperbolic trajectories and homoclinic transversal intersections of their separatrices is discussed. This method is realized for some values of the system paremeters and it is found out that homoclinic invariants corresponding to these(More)