One of the central questions addressed in the project Ready was that of how a system can automatically recognize situationally determined resource limitations of its user--in particular, time pressure and cognitive load. This chapter summarizes most of the work done in Ready on this topic, presenting as well some previously unpublished results. We first consider why on-line recognition or resource limitations can be useful by discussing the ways in which a system might adapt its behavior to perceived resource limitations. We then summarize a number of approaches to the recognition problem that have been taken in Ready and other projects, before focusing on one particular approach: the analysis of features of a user`s speech. In each of two similarly structured experiments, we created four experimental conditions that varied in terms of whether the user was (a) required to produce spoken utterances quickly or not; and (b) navigating within a simulated airport terminal or standing still. In the second experiment, additional distraction was caused by continuous loudspeaker announcements. The speech produced by the experimental subjects (32 in each experiment) was coded in terms of 7 variables. We report on the extent to which each of these variables was influenced by the subjects` resource limitations. We also 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 yield information about the accuracy that can be attained in this way and about the diagnostic value of some specific features of speech.
Assessment of a User`s Time Pressure and Cognitive Load on the Basis of Features of Speech
Jameson, Anthony David;
2009-01-01
Abstract
One of the central questions addressed in the project Ready was that of how a system can automatically recognize situationally determined resource limitations of its user--in particular, time pressure and cognitive load. This chapter summarizes most of the work done in Ready on this topic, presenting as well some previously unpublished results. We first consider why on-line recognition or resource limitations can be useful by discussing the ways in which a system might adapt its behavior to perceived resource limitations. We then summarize a number of approaches to the recognition problem that have been taken in Ready and other projects, before focusing on one particular approach: the analysis of features of a user`s speech. In each of two similarly structured experiments, we created four experimental conditions that varied in terms of whether the user was (a) required to produce spoken utterances quickly or not; and (b) navigating within a simulated airport terminal or standing still. In the second experiment, additional distraction was caused by continuous loudspeaker announcements. The speech produced by the experimental subjects (32 in each experiment) was coded in terms of 7 variables. We report on the extent to which each of these variables was influenced by the subjects` resource limitations. We also 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 yield information about the accuracy that can be attained in this way and about the diagnostic value of some specific features of speech.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
