Knowledge-based systems support for information centers
Journal of Management Information Systems - Special Issue: Decision Support and Knowledge-based Systems
MedSpeak: report creation with continuous speech recognition
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Automatic speech recognition for generalised time based media retrieval and indexing
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Programming by voice, VocalProgramming
Assets '00 Proceedings of the fourth international ACM conference on Assistive technologies
Multimodal error correction for speech user interfaces
ACM Transactions on Computer-Human Interaction (TOCHI)
Spoken dialogue technology: enabling the conversational user interface
ACM Computing Surveys (CSUR)
Speech recognition in university classrooms: liberated learning project
Proceedings of the fifth international ACM conference on Assistive technologies
A Probabilistic Approach to Confidence Estimation and Evaluation
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Neural - Network Based Measures of Confidence for Word Recognition
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Error-responsive feedback mechanisms for speech recognizers
Error-responsive feedback mechanisms for speech recognizers
Proceedings of the 1st IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Challenges in adopting speech recognition
Communications of the ACM - Multimodal interfaces that flex, adapt, and persist
Beyond n-grams: can linguistic sophistication improve language modeling?
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Automatic speech recognition and its application to information extraction
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
ACM Transactions on Computer-Human Interaction (TOCHI)
Structural event detection for rich transcription of speech
Structural event detection for rich transcription of speech
Natural language access to multiple databases: a model and a prototype
Journal of Management Information Systems - Special section: Toward a theory of business process change management
Journal of Management Information Systems - Special section: Strategic and competitive information systems
Error correction via a post-processor for continuous speech recognition
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Advances in confidence measures for large vocabulary
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Journal of Management Information Systems
Hands-free, speech-based navigation during dictation: difficulties, consequences, and solutions
Human-Computer Interaction
Context-based speech recognition error detection and correction
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Are some speech recognition errors easier to detect than others?
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Third-party error detection support mechanisms for dictation speech recognition
Interacting with Computers
Hype or Ready for Prime Time?: Speech Recognition on Mobile Handheld Devices MASR
International Journal of Handheld Computing Research
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The great potential of speech recognition systems in freeing users' hands while interacting with computers has inspired a variety of promising applications. However, given the performance of the state-of-the-art speech recognition technology today, widespread acceptance of speech recognition technology would not be realistic without designing and developing new approaches to detecting and correcting recognition errors effectively. In seeking solutions to the above problem, identifying cues to error detection (CERD) is central. Our survey of the extant literature on the detection and correction of speech recognition errors reveals that the system-initiated, data-driven approach is dominant, but that heuristics from human users have been largely overlooked. This may have hindered the advance of speech technology. In this research, we propose a user-centered approach to discovering CERD. User studies are carried out to implement the approach. Content analysis of the collected verbal protocols lends itself to a taxonomy of CERD. The CERD discovered in this study can improve our knowledge on CERD by not only validating CERD from a user's perspective but also suggesting promising new CERD for detecting speech recognition errors. Moreover, the analysis of CERD in relation to error types and other CERD provides new insights into the context where specific CERD are effective. The findings of this study can be used to not only improve speech recognition output but also to provide context-aware support for error detection. This will help break the barrier for mainstream adoption of speech technology in a variety of information systems and applications.