Speech Communication - Special issue on acoustic echo control and speech enhancement techniques
Statistical methods for speech recognition
Statistical methods for speech recognition
Writer Adaptation for Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
IEEE Software
A Fuzzy-Syntactic Approach to Allograph Modeling for Cursive Script Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Adaptation in Recognition of Handwritten Alphanumeric Characters
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Comparing Adaptation Techniques for On-Line Handwriting Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Pattern recognition using discriminative feature extraction
IEEE Transactions on Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwriting Recognition Algorithm in Different Languages: Survey
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Behavior detection using confidence intervals of hidden Markov models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An adaptive keyboard with personalized language-based features
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
An approach for real-time recognition of online Chinese handwritten sentences
Pattern Recognition
Activity recognition with finite state machines
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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This paper describes an application of the Minimum Classification Error (MCE) criterion to the problem of recognizing online unconstrained-style characters and words. We describe an HMM-based, character and word-level MCE training aimed at minimizing the character or word error rate while enabling flexibility in writing style through the use of multiple allographs per character. Experiments on a writer-independent character recognition task covering alpha-numerical characters and keyboard symbols show that the MCE criterion achieves more than 30 percent character error rate reduction compared to the baseline Maximum Likelihood-based system. Word recognition results, on vocabularies of 5k to 10k, show that MCE training achieves around 17 percent word error rate reduction when compared to the baseline Maximum Likelihood system.