An investigation of locus equations as a source of relational invariance for stop place categorization

Abstract

Locus equations were investigated as a potential higher-order metric capable of illustrating relational invariance for place of articulation in voiced initial stop consonants independently of vowel context. Locus equations are straight-line regression fits to data points formed by plotting onsets of F2 transitions along the y axis and their corresponding midvowel nuclei along the x axis. Twenty subjects, 10 male and 10 female, produced /b/v/t/, /d/v/t/, and /g/v/t/ tokens for 10 vowel contexts. Each CVC token was repeated in a carrier phrase five times yielding 150 tokens per subject. Formant measures were obtained using the MacSpeech Lab II speech analysis system. Locus equation scatter-plots revealed extremely tight clustering of points around the regression line that were consistent across speakers and gender. Derived slope and y-intercept parameters were significantly different across stop place categories. The relative value of F2onset as it linearly changes in relation to the coarticulatorily produced vowel reflects an acoustic correlate of relational invariance for stop place. A discriminant analysis using F2onset & vowel as predictors showed 82%, 78%, and 67% classification rates for labial, alveolar, and velar place. Using derived slope and y-intercept values as predictors led to 100% classification into stop place categories. A neurobiologically oriented perspective on the invariance issue is explored and a brain-based recognition algorithm for stop place integrating burst and F2 cues is offered. [Work supported by NSF.]