| 1. |
- Deshmukh, Amol, et al.
(författare)
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How expressiveness of a robotic tutor is perceived by children in a learning environment
- 2016
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Ingår i: Proc. 11th ACM/IEEE International Conference on Human Robot Interaction. - Piscataway, NJ : IEEE Press. - 9781467383707 ; , s. 423-424
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Konferensbidrag (refereegranskat)abstract
- We present a study investigating the expressiveness of two different types of robots in a tutoring task. The robots used were i) the EMYS robot, with facial expression capabilities, and ii) the NAO robot, without facial expressions but able to perform expressive gestures. Preliminary results show that the NAO robot was perceived to be more friendly, pleasant and empathic than the EMYS robot as a tutor in a learning environment.
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| 3. |
- Meena, Raveesh, 1981-
(författare)
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Data-driven Methods for Spoken Dialogue Systems : Applications in Language Understanding, Turn-taking, Error Detection, and Knowledge Acquisition
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Spoken dialogue systems are application interfaces that enable humans to interact with computers using spoken natural language. A major challenge for these systems is dealing with the ubiquity of variability—in user behavior, in the performance of the various speech and language processing sub-components, and in the dynamics of the task domain. However, as the predominant methodology for dialogue system development is to handcraft the sub-components, these systems typically lack robustness in user interactions. Data-driven methods, on the other hand, have been shown to offer robustness to variability in various domains of computer science and are increasingly being used in dialogue systems research. This thesis makes four novel contributions to the data-driven methods for spoken dialogue system development. First, a method for interpreting the meaning contained in spoken utterances is presented. Second, an approach for determining when in a user’s speech it is appropriate for the system to give a response is presented. Third, an approach for error detection and analysis in dialogue system interactions is reported. Finally, an implicitly supervised learning approach for knowledge acquisition through the interactive setting of spoken dialogue is presented. The general approach taken in this thesis is to model dialogue system tasks as a classification problem and investigate features (e.g., lexical, syntactic, semantic, prosodic, and contextual) to train various classifiers on interaction data. The central hypothesis of this thesis is that the models for the aforementioned dialogue system tasks trained using the features proposed here perform better than their corresponding baseline models. The empirical validity of this claim has been assessed through both quantitative and qualitative evaluations, using both objective and subjective measures.
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