Variational nonparametric Bayesian Hidden Markov Model

The Hidden Markov Model (HMM) has been widely used in many applications such as speech recognition. A common challenge for applying the classical HMM is to determine the structure of the hidden state space. Based on the Dirichlet Process, a nonparametric Bayesian Hidden Markov Model is proposed, which allows an infinite number of hidden states and uses an infinite number of Gaussian components to support continuous observations. An efficient variational inference method is also proposed and applied on the model. Our experiments demonstrate that the variational Bayesian inference on the new model can discover the HMM hidden structure for both synthetic data and real-world applications.