Barbara Groflmann-Hutter's research while affiliated with Universität des Saarlandes and other places

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Publications (1)

Fig. 1. Environment used in the experiments, with a typical pictorial stimulus. 
Fig. 2. Visualization of the eight conditions realized in Experiments 1 (left) and 2 (right).
Fig. 3. Structure of the dynamic Bayesian network used in the evaluation of recognition accuracy. (Nodes within the two large boxes correspond to temporary variables that index features of the current utterance. Each number in parentheses shows the number of discrete states for the variable in question.)
Recognition of Psychologically Relevant Aspects of Context on the Basis of Features of Speech
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August 2009

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91 Reads

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Barbara Groflmann-Hutter

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Frank Wittig

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Especially in mobile systems, one important part of the context of use involves psychological variables like cognitive load and time pressure. This paper looks at one possible way of capturing such aspects of context: the analysis of the features of the users' speech. In a replication and extension of an earlier study of our group, we created four experimental conditions that varied in terms of whether the user was (a) navigating within a simulated airport terminal or standing still; and (b) subject to time pressure or not. The speech produced by these subjects was coded in terms of 7 variables. We trained dynamic Bayesian networks on the resulting data in order to see how well the information in the users' speech could serve as evidence as to which condition the user had been in. The results give an idea of the accuracy that can be attained in this way, the methods that can be used to implement the classiers, and information about the diagnostic value of some specic features of speech.

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