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

Abstract

Locus equations were investigated as a potential 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 ten vowel contexts. Each CVC token was repeated in a carrier phrase five times yielding 50 tokens per stop place category. Formant measures were obtained using the MacSpeech Lab II speech analysis system. Extremely linear regression functions were found characterized by distinct slopes and y intercepts as a function of place of articulation. A discriminant analysis using F2onset and vowel frequencies 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 developed and a brain-based recognition algorithm for stop place integrating burst and F2 trajectory cues is offered.