Palate-referenced articulatory features for acoustic-to-articulator inversion

An Ji, Michael T. Johnson, Jeffrey Berry

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

The selection of effective articulatory features is an important component of tasks such as acoustic-to-articulator inversion and articulatory synthesis. Although it is common to use direct articulatory sensor measurements as feature variables, this approach fails to incorporate important physiological information such as palate height and shape and thus is not as representative of vocal tract cross section as desired. We introduce a set of articulator feature variables that are palate referenced and normalized with respect to the articulatory working space in order to improve the quality of the vocal tract representation. These features include normalized horizontal positions plus the normalized palatal height of two midsagittal and one lateral tongue sensor, as well as normalized lip separation and lip protrusion. The quality of the feature representation is evaluated subjectively by comparing the variances and vowel separation in the working space and quantitatively through measurement of acoustic-to-articulator inversion error. Results indicate that the palate-referenced features have reduced variance and increased separation between vowels spaces and substantially lower inversion error than direct sensor measures.

Original languageEnglish
Pages (from-to)721-725
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
StatePublished - 2014
Event15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014 - Singapore, Singapore
Duration: Sep 14 2014Sep 18 2014

Bibliographical note

Publisher Copyright:
Copyright © 2014 ISCA.

Keywords

  • Acoustic-to-articulatory inversion
  • Articulatory features
  • Electromagnetic articulography (EMA)

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modeling and Simulation

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