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

Sadao Hiroya; Masaaki Honda

doi:10.1109/TSA.2003.822636

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

Sadao Hiroya, Masaaki Honda

School of Sport Sciences

Research output: Contribution to journal › Article › peer-review

105 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	175-185
Number of pages	11
Journal	IEEE Transactions on Speech and Audio Processing
Volume	12
Issue number	2
DOIs	https://doi.org/10.1109/TSA.2003.822636
Publication status	Published - 2004 Mar 1

Keywords

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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