Fundamentals of speech recognition
Fundamentals of speech recognition
Voice communication with computers: conversational systems
Voice communication with computers: conversational systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten myths of multimodal interaction
Communications of the ACM
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
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Online Recognition of Chinese Characters: The State-of-the-Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Word graph based speech rcognition error correction by handwriting input
Proceedings of the 8th international conference on Multimodal interfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
Hidden Markov models based on multi-space probability distribution for pitch pattern modeling
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Multimodal integration: a biological view
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Multimodal integration-a statistical view
IEEE Transactions on Multimedia
An iterative multimodal framework for the transcription of handwritten historical documents
Pattern Recognition Letters
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We study a specific partial hypothesis fusion problem in sequential data labeling. The problem arises in the multimodal applications where a decision is made by merging complete hypothesis from one input and partial hypothesis from the other. For example, in a pen-aided speech interface, appropriate pen input can provide partial but crucial information. We address the problem in a Bayesian framework, and reformulate the solution as a revised search in a representation. A dynamic programming algorithm is proposed to efficiently solve the partial hypothesis fusion via the graph. It is shown that the computational cost of the graph based partial hypothesis fusion is proportional to the size of the graph, which is highly feasible for a given compact graph. We apply the proposed algorithm to two real applications: an intelligent penbased dictation error correction system and an automatic handwritten character completion with a speech "shortcut". Experimental results show that the algorithm is effective in utilizing the partial information from one modality to enhance the bimodal interface performance.