Markovian Models for Sequential DataYoshua
Hidden Markov Models (HMMs) are statistical models of sequential data that have been used successfully in many machine learning applications, especially for speech recognition. Furthermore , 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 artiicial 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.