Estimation of Articulatory Movements From Speech Acoustics Using an HMM-Based Speech Production Model

Sadao Hiroya, Masaaki Honda

Research output: Contribution to journalArticle

103 Citations (Scopus)


We present a method that determines articulatory movements from speech acoustics using a Hidden Markov Model (HMM)-based speech production model. The model statistically generates speech spectrum and articulatory parameters from a given phonemic string. It consists of HMMs of articulatory parameters for each phoneme and an articulatory-to-acoustic mapping for each HMM state. For a given speech spectrum, maximum a posteriori estimation of the articulatory parameters of the statistical model is presented. The performance on sentences was evaluated by comparing the estimated articulatory parameters with the observed parameters. The average RMS errors of the estimated articulatory parameters were 1.50 mm from the speech acoustics and the phonemic information in an utterance and 1.73 mm from the speech acoustics only.

Original languageEnglish
Pages (from-to)175-185
Number of pages11
JournalIEEE Transactions on Speech and Audio Processing
Issue number2
Publication statusPublished - 2004 Mar 1



  • Articulatory HMM
  • Articulatory-to-acoustic mapping
  • HMM-based speech production model
  • Speech inversion

ASJC Scopus subject areas

  • Software
  • Acoustics and Ultrasonics
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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