  We test the robustness of a maximum-likelihood (ML) based classifier where
sequential data as observation is corrupted by noise. The hypothesis is that a
generative model, that combines the state transitions of a hidden Markov model
(HMM) and the neural network based probability distributions for the hidden
states of the HMM, can provide a robust classification performance. The
combined model is called normalizing-flow mixture model based HMM (NMM-HMM). It
can be trained using a combination of expectation-maximization (EM) and
backpropagation. We verify the improved robustness of NMM-HMM classifiers in an
application to speech recognition.
