Fundamentals of speech recognition
Fundamentals of speech recognition
Voice communication with computers: conversational systems
Voice communication with computers: conversational systems
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
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)
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
A system for dynamic 3D visualisation of speech recognition paths
AVI '08 Proceedings of the working conference on Advanced visual interfaces
Interface design strategies for computer-assisted speech transcription
Proceedings of the 20th Australasian Conference on Computer-Human Interaction: Designing for Habitus and Habitat
Graph-based partial hypothesis fusion for pen-aided speech input
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on multimodal processing in speech-based interactions
Multimodal interactive transcription of text images
Pattern Recognition
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
Visualization of uncertainty in lattices to support decision-making
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
An iterative multimodal framework for the transcription of handwritten historical documents
Pattern Recognition Letters
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We propose a convenient handwriting user interface for correcting speech recognition errors efficiently. Via the proposed hand-marked correction on the displayed recognition result, substitution, deletion and insertion errors can be corrected efficiently by rescoring the word graph generated in the recognition pass. A new path in the graph that matches the user's feedback in the maximum likelihood sense is found.With the aid of language model and hand corrections part in the best decoded path, rescoring the word graph can correct more errors than user provides. All recognition errors can be corrected after finite number of corrections. Experimental results show that by indicating one word error in user feedback, 33.8% of the erroneous sentences can be corrected; while by indicating one character error, 12.9% of the erroneous sentences can be corrected.