Fundamentals of speech recognition
Fundamentals of speech recognition
An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Postprocessing of Recognized Strings Using Nonstationary Markovian Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Diffusion of context and credit information in Markovian models
Journal of Artificial Intelligence Research
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
Improved hidden Markov models in the wavelet-domain
IEEE Transactions on Signal Processing
An MCMC sampling approach to estimation of nonstationary hiddenMarkov models
IEEE Transactions on Signal Processing
Structural hidden Markov models for biometrics: Fusion of face and fingerprint
Pattern Recognition
A statistical multiresolution approach for face recognition using structural hidden Markov models
EURASIP Journal on Advances in Signal Processing
An Efficient Wavelet Based Feature Extraction Method for Face Recognition
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Conformation-based hidden Markov models: application to human face identification
IEEE Transactions on Neural Networks
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We introduce in this paper a generalization of the widely used hidden Markov models (HMM's), which we name "structural hidden Markov models" (SHMM). Our approach is motivated by the need of modeling complex structures which are encountered in many natural sequences pertaining to areas such as computational molecular biology, speech/handwriting recognition and content-based information retrieval. We consider observations as strings that produce the structures derived by an unsupervised learning process. These observations are related in the sense they all contribute to produce a particular structure. Four basic problems are assigned to a structural hidden Markov model: (1) probability evaluation, (2) state decoding, (3) structural decoding, and (4) parameter re-estimation. We have applied our methodology to recognize handwritten numerals. The results reported in this application show that the structural hidden Markov model outperforms the traditional hidden Markov model with a 23.9% error-rate reduction.