Speech recognition can be a powerful tool for individuals with physical disabilities that hinder their ability to use traditional input devices. State-of-the-art speech recognition systems typically provide mechanisms for both data entry and cursor control, but the researchers continue to investigate methods of improving these interactions. Numerous researchers are investigating methods to improve the underlying technologies that make speech recognition possible and others focus on understanding the difficulties users experience using dictation-oriented applications, but few researchers have investigated the issues involved in speech-based cursor control. In this article, we describe a study that investigates the efficacy of two variations of a standard speech-based cursor control mechanism. One employs the standard mouse cursor while the second provides a predictive cursor designed to help users compensate for the delays often associated with speech recognition. As expected, larger targets and shorter distances resulted in shorter target selection times while larger targets also resulted in fewer errors. Although there were no differences between the standard and predictive cursors, a relationship between the delays associated with spoken input, the speed at which the cursor moves, and the minimum size for targets that can be reliably selected emerged that can guide the application of similar speechbased cursor control mechanisms as well as future research.

Speech recognition is an important technology that is becoming increasingly effective for dictationoriented activities. While recognition accuracy has increased dramatically in recent years, recent studies confirm that traditional computer users are still faster using a keyboard and mouse and spend more time correcting errors than dictating. Further, as these users become more experienced they frequently adopt multimodal strategies that require the keyboard and mouse when correcting errors. While speech recognition can be a convenient alternative for traditional computer users, it can be a powerful tool for individuals with physical disabilities that limit their ability to use a keyboard and mouse. However, research into the performance, satisfaction, and usage patterns of individuals with physical disabilities has not been reported. In this article, we report on a study that provides initial insights into the efficacy of existing speech recognition systems with respect to individuals with physical disabilities. Our results confirm that productivity does not differ between traditional users and those with physical disabilities. In contrast, numerous differences were observed when users rated their satisfaction with the system and when usage patterns were analyzed.

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Speech recognition can be a powerful tool when physical disabilities, environmental factors, or the tasks in which an individual is engaged hinders the individual's ability to use traditional input devices. While state-of-theart speech-recognition systems typically provide mechanisms for both data entry and cursor control, speech-based interactions continue to be slow when compared to similar keyboard-or mouse-based interactions. Although numerous researchers continue to investigate methods of improving speech-based interactions, most of these efforts focus on the underlying technologies or dictation-oriented applications. As a result, the efficacy of speech-based cursor control has received little attention. In this article, we describe two experiments that provide insight into the issues involved when using speech-based cursor control. The first compares two variations of a common speech-based cursorcontrol mechanism. One employs the standard mouse cursor while the second provides a predictive cursor designed to help users compensate for the delays often associated with speech recognition. As expected, larger targets and shorter distances resulted in shorter target selection times, while larger targets also resulted in fewer errors. Interestingly, there were no differences between the standard and predictive cursors. The second experiment investigates the delays associated with spoken input, explains why the original predictive-cursor implementation failed to provide the expected benefits, and provides insight that guided the design of a new predictive cursor.