Markovian Models for Sequential Data

Hidden Markov Models HMMs are statistical models of sequential data that have been used successfully in many machine learning applications especially for speech recognition Further more in the last few years many new and promising probabilistic models related to HMMs have been proposed We rst summarize the basics of HMMs and then review several recent related learning algorithms and extensions of HMMs including in particular hybrids of HMMs with arti cial neural networks Input Output HMMs which are conditional HMMs using neu ral networks to compute probabilities weighted transducers variable length Markov models and Markov switching state space models Finally we discuss some of the challenges of future research in this very active area

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