Patterns of entry and correction in large vocabulary continuous speech recognition systems
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Multimodal error correction for speech user interfaces
ACM Transactions on Computer-Human Interaction (TOCHI)
Overcoming unusability: developing efficient strategies in speech recognition systems
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Improving speech playback using time-compression and speech recognition
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Error correction of voicemail transcripts in SCANMail
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Evaluating assistance of natural language policy authoring
Proceedings of the 4th symposium on Usable privacy and security
Parakeet: a continuous speech recognition system for mobile touch-screen devices
Proceedings of the 14th international conference on Intelligent user interfaces
Hype or Ready for Prime Time?: Speech Recognition on Mobile Handheld Devices MASR
International Journal of Handheld Computing Research
Aiding human discovery of handwriting recognition errors
Proceedings of the 15th ACM on International conference on multimodal interaction
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In a typical speech dictation interface, the recognizer's best-guess is displayed as normal, unannotated text. This ignores potentially useful information about the recognizer's confidence in its recognition hypothesis. Using a confidence measure (which itself may sometimes be inaccurate), we investigated providing visual feedback about low-confidence portions of the recognition using shaded, red underlining. An evaluation showed, compared to a baseline without underlining, underlining low-confidence areas did not increase user's speed or accuracy in detecting errors. However, we found that when recognition errors were correctly underlined, they were discovered significantly more often than baseline. Conversely, when errors failed to be underlined, they were discovered less often. Our results indicate confidence visualization can be effective --- but only if the confidence measure has high accuracy. Further, since our results show that users tend to trust confidence visualization, designers should be careful in its application if a high accuracy confidence measure is not available.