Buried Markov models: a graphical-modeling approach to automatic speech recognition

@article{Bilmes2003BuriedMM,
  title={Buried Markov models: a graphical-modeling approach to automatic speech recognition},
  author={Jeff A. Bilmes},
  journal={Computer Speech & Language},
  year={2003},
  volume={17},
  pages={213-231}
}
In this work, buried Markov models (BMM) are introduced. In a BMM, a Markov chain state at time t determines the conditional independence patterns that exist between random variables lying within a local time window surrounding t. This model is motivated by and can be fully described by ‘‘graphical models’’, a general technique to describe families of probability distributions. In the paper, it is shown how information-theoretic criterion functions can be used to induce sparse, discriminative… CONTINUE READING
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