Patterns of entry and correction in large vocabulary continuous speech recognition systems
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Taming recognition errors with a multimodal interface
Communications of the ACM
Dasher—a data entry interface using continuous gestures and language models
UIST '00 Proceedings of the 13th annual ACM symposium on User interface software and technology
Multimodal error correction for speech user interfaces
ACM Transactions on Computer-Human Interaction (TOCHI)
Humsher: a predictive keyboard operated by humming
The proceedings of the 13th international ACM SIGACCESS conference on Computers and accessibility
Measuring the performance of gaze and speech for text input
Proceedings of the Symposium on Eye Tracking Research and Applications
Speak up your mind: using speech to capture innovative ideas on interactive surfaces
Proceedings of the 10th Brazilian Symposium on on Human Factors in Computing Systems and the 5th Latin American Conference on Human-Computer Interaction
SpeeG: a multimodal speech- and gesture-based text input solution
Proceedings of the International Working Conference on Advanced Visual Interfaces
Easier mobile phone input using the jusfone keyboard
ICCHP'12 Proceedings of the 13th international conference on Computers Helping People with Special Needs - Volume Part II
SpeeG2: a speech- and gesture-based interface for efficient controller-free text input
Proceedings of the 15th ACM on International conference on multimodal interaction
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Speech Dasher allows writing using a combination of speech and a zooming interface. Users first speak what they want to write and then they navigate through the space of recognition hypotheses to correct any errors. Speech Dasher's model combines information from a speech recognizer, from the user, and from a letter-based language model. This allows fast writing of anything predicted by the recognizer while also providing seamless fallback to letter-by-letter spelling for words not in the recognizer's predictions. In a formative user study, expert users wrote at 40 (corrected) words per minute. They did this despite a recognition word error rate of 22%. Furthermore, they did this using only speech and the direction of their gaze (obtained via an eye tracker).