A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Fundamentals of speech recognition
Fundamentals of speech recognition
Statistical methods for speech recognition
Statistical methods for speech recognition
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Dual hidden Markov model for characterizing wavelet coefficients from multi-aspect scattering data
Signal Processing - Special section on information theoretic aspects of digital watermarking
Infrared-Image Classification Using Hidden Markov Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning in Computer Vision (Computational Imaging and Vision)
Machine Learning in Computer Vision (Computational Imaging and Vision)
Segmental Hidden Markov Models with Random Effects for Waveform Modeling
The Journal of Machine Learning Research
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Computational methods for hidden Markov tree models-an application to wavelet trees
IEEE Transactions on Signal Processing
Improved hidden Markov models in the wavelet-domain
IEEE Transactions on Signal Processing
Hidden Markov models for wavelet-based blind source separation
IEEE Transactions on Image Processing
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The wavelet transform has been used for feature extraction in many applications of pattern recognition. However, in general the learning algorithms are not designed taking into account the properties of the features obtained with discrete wavelet transform. In this work we propose a Markovian model to classify sequences of frames in the wavelet domain. The architecture is a composite of an external hidden Markov model in which the observation probabilities are provided by a set of hidden Markov trees. Training algorithms are developed for the composite model using the expectation-maximization framework. We also evaluate a novel delay-invariant representation to improve wavelet feature extraction for classification tasks. The proposed methods can be easily extended to model sequences of images. Here we present phoneme recognition experiments with TIMIT speech corpus. The robustness of the proposed architecture and learning method was tested by reducing the amount of training data to a few patterns. Recognition rates were better than those of hidden Markov models with observation densities based in Gaussian mixtures.