Third International Conference on Spoken Language Processing (ICSLP 94)
This paper models speech recognition as the estimation of distinctive feature values at articulatory landmarks . 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.
Bibliographic reference. Johnson, Mark (1994): "Automatic context-sensitive measurement of the acoustic correlates of distinctive features at landmarks", In ICSLP-1994, 1639-1642.