Hidden-articulator Markov models for speech recognition

  title={Hidden-articulator Markov models for speech recognition},
  author={Matthew Richardson and Jeff A. Bilmes and Chris Diorio},
  journal={Speech Communication},
In traditional speech recognition using Hidden Markov Models (HMMs), each state represents an acoustic portion of a phoneme. We explore the concept of an articulator based HMM, where each state represents a particular articulatory configuration [Erler 1996]. In this paper, we present a novel articulatory feature mapping and a new technique for model initialization. In addition, we use diphone modeling which allows context dependent training of transition probabilities. Our goal is to confirm… CONTINUE READING
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