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Investigating the impact of interlocutor voice on syntactic alignment in human–computer dialogue

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The 26th BCS Conference on Human Computer Interaction (HCI)

Human Computer Interaction

12 - 14 September 2012

Syntactic Alignment, Human-computer Dialogue, Satisfaction, Controlled Experiment, Psycholinguistics

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      Abstract

      Language is at the core of most social activity. Psycholinguistic research has shown that our conversational partners influence our linguistic choices be it syntactic or lexical, a concept termed alignment. As our interaction with computer interlocutors become more frequent recent efforts have been made to understand how and what impacts alignment with computers, showing that our perceptions of computer systems impact on alignment with computer interlocutors. This work looks to identify the impact of how spoken dialogue system design characteristics, specifically system voice type, impact user linguistic behaviour in terms of syntactic alignment in human-computer dialogue. Additionally we wished to identify whether syntactic alignment levels can be used as a behavioural indicator of interaction satisfaction. The research used a wizard of oz experiment design paired with a confederate-scripting paradigm commonly used in psycholinguistics research. We found that there was no significant effect of voice type on syntactic alignment, although there was a significant effect of voice type on interaction satisfaction. Participants rated their experiences with a basic computer voice significantly lower in satisfaction compared to human based and advanced voice computer conditions. The results are discussed in terms of the conceptual nature of syntactic alignment and the impact of item stimuli on alignment levels. Future plans for research are also discussed.

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      Most cited references 9

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      This paper identifies several serious problems with the widespread use of ANOVAs for the analysis of categorical outcome variables such as forced-choice variables, question-answer accuracy, choice in production (e.g. in syntactic priming research), et cetera. I show that even after applying the arcsine-square-root transformation to proportional data, ANOVA can yield spurious results. I discuss conceptual issues underlying these problems and alternatives provided by modern statistics. Specifically, I introduce ordinary logit models (i.e. logistic regression), which are well-suited to analyze categorical data and offer many advantages over ANOVA. Unfortunately, ordinary logit models do not include random effect modeling. To address this issue, I describe mixed logit models (Generalized Linear Mixed Models for binomially distributed outcomes, Breslow & Clayton, 1993), which combine the advantages of ordinary logit models with the ability to account for random subject and item effects in one step of analysis. Throughout the paper, I use a psycholinguistic data set to compare the different statistical methods.
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            Author and article information

            Affiliations
            HCI Centre

            School of Computer Science

            University of Birmingham

            Edgbaston

            B15 2TT
            Department of Psychology

            7 George Square

            University of Edinburgh

            Edinburgh

            EH8 9JZ
            Contributors
            Conference
            September 2012
            September 2012
            : 39-48
            10.14236/ewic/HCI2012.9
            © Benjamin R. Cowan et al. Published by BCS Learning and Development Ltd. The 26th BCS Conference on Human Computer Interaction, Birmingham, UK

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            The 26th BCS Conference on Human Computer Interaction
            HCI
            26
            Birmingham, UK
            12 - 14 September 2012
            Electronic Workshops in Computing (eWiC)
            Human Computer Interaction
            Product
            Product Information: 1477-9358 BCS Learning & Development
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

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