Evaluating a synthetic talking head using a dual task : modality effects on speech understanding and cognitive load

Catherine J. Stevens, Guillaume Gibert, Yvonne Leung, Zhengzhi Zhang

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    12 Citations (Scopus)

    Abstract

    The dual task is a data-rich paradigm for evaluating speech modes of a synthetic talking head. Three experiments manipulated auditory-visual (AV) and auditory-only (A-only) speech produced by text-to-speech synthesis from a talking head (Experiment 1 - single task; Experiment 2 - dual task), and natural speech produced by a human male similar in appearance to the talking head (Experiment 3 - dual task). In a dual task, participants perform two tasks concurrently with a secondary reaction time (RT) task sensitive to cognitive processing demands of the primary task. In the primary task, participants either shadowed words or named the superordinate categories to which words belonged under AV (dynamic face with lips moving) or A-only (static face) speech modes. First, it was hypothesized that category naming is more difficult than shadowing. The hypothesis was supported in each experiment with significantly longer latencies on the primary task and slower RT on the secondary task. Second, an AV advantage was hypothesized and supported by significantly shorter latencies for the AV modality on the primary task of Experiment 3 and with partial support in Experiment 1. Third, it was hypothesized that while the AV modality helps it also creates great cognitive load. Significantly longer RT for AV presentation in the secondary tasks supported this hypothesis. The results indicate that task difficulty influences speech perception. Performance on a secondary task can reveal cognitive demand that is not evident in a single task or self-report ratings. A dual task will be an effective evaluation tool in operational environments where multiple tasks are conducted (e.g., responding to spoken directions and monitoring displays) and an implicit, sensitive measure of cognitive load is imperative.
    Original languageEnglish
    Pages (from-to)440-454
    Number of pages21
    JournalInternational Journal of Human-Computer Studies
    Volume71
    Issue number4
    DOIs
    Publication statusPublished - 2013

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