Uncertainty is a frequently occurring affective state that learners ex-perience during the acquisition of a second language. This state canconstitute both a learning opportunity and a source of learner frus-tration. An appropriate detection could therefore benefit the learn-ing process by reducing cognitive instability. In this study, we usea dyadic practice conversation between an adult second-languagelearner and a social robot to elicit events of uncertainty throughthe manipulation of the robot’s spoken utterances (increased lex-ical complexity or prosody modifications). The characteristics ofthese events are then used to analyze multi-party practice conver-sations between a robot and two learners. Classification models aretrained with multimodal features from annotated events of listener(un)certainty. We report the performance of our models on differentsettings, (sub)turn segments and multimodal inputs. ; QC 20200930 ; Collaborative Robot Assisted Language Learning


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    Title :

    Detection of Listener Uncertainty in Robot-Led Second Language Conversation Practice


    Contributors:

    Publication date :

    2020-01-01



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    004 / 629




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