Fundamentals of speech recognition
Fundamentals of speech recognition
Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Full English Sentence Database for Off-Line Handwriting Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
The Lincoln tied-mixture HMM continuous speech recognizer
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Speech driven head motion synthesis based on a trajectory model
ACM SIGGRAPH 2007 posters
Large-Margin Discriminative Training of Hidden Markov Models for Speech Recognition
ICSC '07 Proceedings of the International Conference on Semantic Computing
Hidden Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large margin training of acoustic models for speech recognition
Large margin training of acoustic models for speech recognition
Matrix updates for perceptron training of continuous density hidden Markov models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Large margin training for hidden Markov models with partially observed states
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A novel framework and training algorithm for variable-parameter hidden Markov models
IEEE Transactions on Audio, Speech, and Language Processing
Joint Optimization of Hidden Conditional Random Fields and Non Linear Feature Extraction
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Discriminative learning for minimum error classification [patternrecognition]
IEEE Transactions on Signal Processing
A Study of Variable-Parameter Gaussian Mixture Hidden Markov Modeling for Noisy Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
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There are two popular families of statistical models for dealing with sequences and in particular with handwriting signals, either on-line or off-line, the well known generative hidden Markov models and the more recently proposed discriminative Hidden Conditional Random Fields. One key issue in such modeling frameworks is to efficiently handle variability. The traditional approach consists in first removing as much as possible signal variability in the preprocessing stage, and to use more complex models, for instance in the case of hidden Markov models one increases the number of states and the Gaussian mixture size. We focus here on another kind of approaches where the probability distribution implemented by the models depends on a number of additional contextual variables, that are assumed fixed or that vary slowly along a sequence. The context may stand for emotion features in speech recognition, physical features in gesture recognition, gender, age, etc. We propose a framework for deriving markovian models that make use of such contextual information. This yields new models that we call Contextual hidden Markov models and contextual Hidden Conditional Random Fields. We detail learning algorithms for both models and investigate their performances on the IAM off-line handwriting dataset.