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Ann Marie Vosburgh

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Publications by Ann Marie Vosburgh (bibliography)

1989

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Brown, Norman R. and Vosburgh, Ann Marie (1989): Evaluating the Accuracy of a Large-Vocabulary Speech Recognition System. In: Proceedings of the Human Factors Society 33rd Annual Meeting 1989. pp. 296-300.

“The Tangora is a large-vocabulary, speaker-dependent, isolated-word speech recognition system. In this paper, we describe a study designed to test this system under a broader range of conditions than had previously been considered. The experiment itself consisted of four experimental sessions: Sessions 1 and 4 were enrollment sessions, and Sessions 2 and 3 were test sessions. During each test session, participants read 32 preselected sentences and dictated 40 sentences of their own composition The results of this experiment indicated that (a) there was a high degree of inter-subject variability and a high degree of intra-subject consistency; (b) users did not improve with limited experience; (c) the style/content of the test sentences affected recognition performance; (d) recognition errors were more common following misrecognized words than following correctly recognized words; and (e) new users had little difficulty with isolated-word speech. We discuss the implications that these findings have for application selection, interface design, user training, and system evaluation.”

1988

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Gould, John D., Boies, Stephen J., Meluson, Mia, Rasamny, Marwan and Vosburgh, Ann Marie (1988): Empirical Evaluation of Entry and Selection Methods for Specifying Dates. In: Proceedings of the Human Factors Society 32nd Annual Meeting 1988. pp. 279-283.

“Experienced and inexperienced computer users used seven different interaction methods to specify dates of events. Key results were that the three entry methods were faster, more accurate, and preferred over the four selection methods -- by both experienced and inexperienced computer users. The rank order of performance with these methods was about the same for both groups. Number of keystrokes required by each method was a good predictor of performance time. For selection tasks, decomposing them into separate fields is advisable.”

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