HIDDEN MARKOV MODELS
Statistical Models for Non-Stationary Processes Hidden Markov Models Training Hidden Markov Models Decoding of Signals Using Hidden Markov Models HMM-Based Estimation of Signals in Noise Signal and Noise Model Combination and Decomposition HMM-Based Wiener Filters Summary
idden Markov models (HMMs) are used for the statistical modelling of non-stationary signal processes such as speech signals, image sequences and time-varying noise. An HMM models the time variations (and/or the space variations) of the statistics of a random process with a Markovian chain of state-dependent stationary subprocesses. An HMM is essentially a Bayesian finite state process, with a Markovian prior for modelling...