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Danis, Catalina; Comerford, Liam D.; Janke, Eric; Davies, K.; Vries, Jackie De; Bertrand, A.

doi:10.1145/259963.260490

Cited by 17 publications

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“…The latter approach has at least two problems: The correct word may be far down the list or the correct word may not appear in the list. Danis et al [6] used a similar approach where the voice was the primary modality for text entry and the mouse was a secondary modality providing spatial information for locating and selecting the incorrect word. Sindhwani et al [39] investigated error corrections in a multimodal environment by combining gaze with traditional keyboards.…”

Section: Integration Of Additional Modalities For Error Correctionmentioning

confidence: 99%

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Leveraging Error Correction in Voice-based Text Entry by Talk-and-Gaze

Sengupta

Bhattarai

Sarcar

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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We present the design and evaluation of Talk-and-Gaze (TaG), a method for selecting and correcting errors with voice and gaze. TaG uses eye gaze to overcome the inability of voiceonly systems to provide spatial information. The user's point of gaze is used to select an erroneous word either by dwelling on the word for 800 ms (D-TaG) or by uttering a "select" voice command (V-TaG). A user study with 12 participants compared D-TaG, V-TaG, and a voice-only method for selecting and correcting words. Corrections were performed more than 20% faster with D-TaG compared to the V-TaG or voice-only methods. As well, D-TaG was observed to require 24% less selection effort than V-TaG and 11% less selection effort than voice-only error correction. D-TaG was well received in a subjective assessment with 66% of users choosing it as their preferred choice for error correction in voice-based text entry.

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Section: Integration Of Additional Modalities For Error Correctionmentioning

confidence: 99%

“…The experiments were conducted in an environment with controlled ambient light and sound. The software to evaluate the interactions was made on React Native 6 which recorded the participant's performance. Data were stored in a .csv file for further evaluation.…”

Section: Apparatusmentioning

confidence: 99%

Leveraging Error Correction in Voice-based Text Entry by Talk-and-Gaze

Sengupta

Bhattarai

Sarcar

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Speech User Interface Evolution

Karat¹,

Lai²,

Danis³

et al. 1999

Human Factors and Voice Interactive Systems

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Untitled

Sears¹,

Lin²,

Karimullah³

2002

UAIS

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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.

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Abstract: YorktownHeights, NY 10598 danis@watson.ibm. com Repetitive Stress Injury (RSI), a disorder that curtails

Cited by 17 publications

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Leveraging Error Correction in Voice-based Text Entry by Talk-and-Gaze

Leveraging Error Correction in Voice-based Text Entry by Talk-and-Gaze

Speech User Interface Evolution

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