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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)
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)
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)
This paper investigates the connection between intelligibil-ity and pronunciation accuracy. We compare which words in non-native English speech are likely to be misrecognized and which words are likely to be marked as pronunciation errors. We found that only 16% of the variability in word-level intel-ligibility can be explained by the presence of obvious(More)