2-D Shape Classification Using Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of speech recognition
Fundamentals of speech recognition
Nonstationary hidden Markov model
Signal Processing
Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keyword Spotting in Poorly Printed Documents using Pseudo 2-D Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
IEEE Transactions on Multimedia
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
Experiments on the application of IOHMMs to model financial returns series
IEEE Transactions on Neural Networks
ACM Transactions on Asian Language Information Processing (TALIP)
Short note on two output-dependent hidden Markov models
Pattern Recognition Letters
A stochastic HMM-based forecasting model for fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In the standard hidden Markov model, the current state depends only on the immediately preceding state, but has nothing to do with the immediately preceding observation. This paper presents a new type of hidden Markov models in which the current state depends both on the immediately preceding state and the immediately preceding observation, and the state sequence is still a Markov chain. Several new algorithms are given and simulated for the three basic problems of interest, including probability evaluation, optimal state sequence and parameter estimation. One example of its initial applications shows that the new model may outperform the standard model in some circumstance.