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arXiv · 2102.07284

Robust Classification using Hidden Markov Models and Mixtures of Normalizing Flows

Anubhab Ghosh·Antoine Honoré·Dong Liu·Gustav Eje Henter·Saikat Chatterjee
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Abstract

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.

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Anubhab Ghosh, Antoine Honoré, Dong Liu, Gustav Eje Henter, Saikat Chatterjee. 2021-02-15. Robust Classification using Hidden Markov Models and Mixtures of Normalizing Flows. https://doi.org/10.1109/mlsp49062.2020.9231775

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