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Robust Classification using Hidden Markov Models and Mixtures of Normalizing Flows

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Anubhab Ghosh, Antoine Honoré, Dong Liu, Gustav Eje Henter, Saikat Chatterjee

Machine Learning · Submitted 15 Feb 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We verify the improved robustness of NMM-HMM classifiers in an application to speech recognition.

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arXiv ID2102.07284 (version 1)
Pith IDK5M5MIHA, Pith's permanent record of this paper and its review
CitationAntoine Honor\'e, Anubhab Ghosh, Dong Liu, Gustav Eje Henter, Saikat Chatterjee. "Robust Classification using Hidden Markov Models and Mixtures of Normalizing Flows." arXiv:2102.07284 (2021). https://pith.science/paper/K5M5MIHA
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,221 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.08 · human participants 0.09 · controlled comparison 0.37 · randomized assignment 0.02 · states quantitative result 0.04 · compares with prior results 0.34 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We test the robustness of a maximum-likelihood (ML) based classifier where sequential data as observation is corrupted by noise. background 0.52 0.90 0.04 asserted 0.79
s1 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. contribution 0.48 0.18 0.37 hedged possibility 0.82
s2 The combined model is called normalizing-flow mixture model based HMM (NMM-HMM). method 0.83 0.62 0.05 asserted 0.99
s3 It can be trained using a combination of expectation-maximization (EM) and backpropagation. method 1.00 0.55 0.03 asserted 0.88
s4 We verify the improved robustness of NMM-HMM classifiers in an application to speech recognition. result 0.94 0.94 0.69 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.99 · evidence from observation or experiment 0.60.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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