  Hidden Markov model (HMM) has been successfully used for sequential data
modeling problems. In this work, we propose to power the modeling capacity of
HMM by bringing in neural network based generative models. The proposed model
is termed as GenHMM. In the proposed GenHMM, each HMM hidden state is
associated with a neural network based generative model that has tractability
of exact likelihood and provides efficient likelihood computation. A generative
model in GenHMM consists of mixture of generators that are realized by flow
models. A learning algorithm for GenHMM is proposed in expectation-maximization
framework. The convergence of the learning GenHMM is analyzed. We demonstrate
the efficiency of GenHMM by classification tasks on practical sequential data.
Code available at https://github.com/FirstHandScientist/genhmm.
