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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)
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)
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)
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)
—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)
Over several years, we have developed an approach to spoken dialogue systems that includes rule-based and trainable dialogue managers, spoken language understanding and generation modules, and a comprehensive dialogue system architecture. We present a Reinforcement Learning-based dialogue system that goes beyond standard rule-based models and computes(More)
Speech recognition engines should remain reasonably accurate in adverse environments in order to find their ways from laboratories towards applications. However the human auditory system has been proven to be a versatile tool, which is capable of outperforming the known artificial algorithms in their target environments. Recent advances in psychoacoustics(More)
In this paper we have studied the problem of detecting the spoken turn boundaries in human-human spoken conversations. The automation of this task is essential to enable the analysis, recognition and understanding of the speech transcriptions and dialog structures (e.g. turn taking, dialog act segmentation etc.). The problem formulation is different from(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)