Corpus ID: 28558623

Inverted HMM - a Proof of Concept

@inproceedings{Doetsch2016InvertedH,
  title={Inverted HMM - a Proof of Concept},
  author={Patrick Doetsch and Hermann Ney and Stefan Hegselmann and Ralf Schl{\"u}ter},
  booktitle={NIPS 2016},
  year={2016}
}
In this work, we propose an inverted hidden Markov model (HMM) approach to automatic speech and handwriting recognition that naturally incorporates discriminative, artificial neural network based label distributions. [...] Key Method The approach does not assume the usual decomposition into a separate (generative) acoustic model and a language model, and allows for a variety of model assumptions, incl. statistical variants of attention.Expand
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6 Citations

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This work reviews neural segmental models, which can be viewed as consisting of a neural network-based acoustic encoder and a finite-state transducer decoder and explores training approaches, including multistage versus end-to-end training and multitask training that combines segmental and frame-level losses. Expand
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