ISCA Archive ICSLP 1994
ISCA Archive ICSLP 1994

Automatic context-sensitive measurement of the acoustic correlates of distinctive features at landmarks

Mark Johnson

This paper models speech recognition as the estimation of distinctive feature values at articulatory landmarks [8]. Toward this end, we propose modeling each distinctive feature as a table containing phonetic contexts, a list of signal measurements (acoustic correlates) which provide information about the feature in each context, and, for each context, a statistical model for evaluating the feature given the measurements. The model of a distinctive feature may include several sets of acoustic correlates, each indexed by a different set of context features. Context features are typically lower-level features of the same segment, e.g. manner features ([continuant, sonorant]) provide context for the identification of articulator-bound features ([lips, blade]). The acoustic correlates of a feature can be any static or dynamic spectral measurements defined relative to the time of the landmark. The statistical model is a simple N-dimensional Gaussian hypothesis test. A measurement program has been developed to test the usefulness of user-defined acoustic correlates in user-defined phonetic contexts. Measures of voice onset time and formant locus classification are presented as examples.


Cite as: Johnson, M. (1994) Automatic context-sensitive measurement of the acoustic correlates of distinctive features at landmarks. Proc. 3rd International Conference on Spoken Language Processing (ICSLP 1994), 1639-1642

@inproceedings{johnson94_icslp,
  author={Mark Johnson},
  title={{Automatic context-sensitive measurement of the acoustic correlates of distinctive features at landmarks}},
  year=1994,
  booktitle={Proc. 3rd International Conference on Spoken Language Processing (ICSLP 1994)},
  pages={1639--1642}
}