@inproceedings{ghosh2020robust,
  title={Robust classification using hidden {M}arkov models and mixtures of normalizing flows},
  author={Ghosh, Anubhab and Honor{\'e}, Antoine and Liu, Dong and Henter, Gustav Eje and Chatterjee, Saikat},
  booktitle={Proc. MLSP},
  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.},
  keywords={speech recognition, generative models, hidden Markov models, neural networks},
  address={Espoo, Finland},
  month={Sept.},
  publisher={IEEE},
  volume={30},
  pages={1--6},
  doi={10.1109/MLSP49062.2020.9231775},
  year={2020}
}