Feedback strategies for error correction in speech recognition systems
International Journal of Man-Machine Studies
Data-entry by voice: facilitating correction of misrecognitions
Interactive speech technology
Multimodal error correction for speech user interfaces
ACM Transactions on Computer-Human Interaction (TOCHI)
Designing habitable dialogues for speech-based interaction with computers
International Journal of Human-Computer Studies
Simultaneous Tracking of Head Poses in a Panoramic View
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Eye gaze and speech for data entry: a comparison of different data entry methods
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Crossmodal error correction of continuous handwriting recognition by speech
Proceedings of the 12th international conference on Intelligent user interfaces
Error Rate in Multimodal Mobility Systems
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Combining modality theory and context models
PIT'06 Proceedings of the 2006 international tutorial and research conference on Perception and Interactive Technologies
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In the work carried out earlier [1][2], it was found that an eye gaze and speech enabled interface was the most preferred form of data entry method when compared to other methods such as mouse and keyboard, handwriting and speech only. It was also found that several non-native United Kingdom (UK) English speaking speakers did not prefer the eye gaze and speech system due to the low success rate caused by the inaccuracy of the speech recognition component. Hence in order to increase the usability of the eye gaze and speech data entry system for these users, error recovery methods are required. In this paper we present three different multimodal interfaces that employ the use of speech recognition and eye gaze tracking within a virtual keypad style interface to allow for the use of error recovery (re-speak with keypad, spelling with keypad and re-speak and spelling with keypad). Experiments show that through the use of this virtual keypad interface, an accuracy gain of 10.92% during first attempt and 6.20% during re-speak by non-native speakers in ambiguous fields (initials, surnames, city and alphabets) can be achieved [3]. The aim of this work is to investigate whether the usability of the eye gaze and speech system can be improved through one of these three multimodal blended multimodal error recovery methods.