Locus equations, which describe linear relationships between the onset and steady-state formant values in consonant-vowel syllables, have recently been measured using a large quantity of acoustic data and have been proposed as a source of relational invariance for stop place categorization (H. Sussman et al., 1991). In this paper we present a statistical model which utilizes the conceptualization of the locus equations as a basis for parametric of modeling of phonetic contexts — place of articulation, and of their acoustic consequences — formant transitions. The model is based on a hidden Markov model representation of formant-transition microsegments of speech. We develop a generalized EM algorithm for automatic estimation of the model parameters. The proposed model is capable of generalizing consonant characteristics from a small training data set where the contextual information is only sparsely represented, and is hence applicable to solving very large vocabulary speech recognition problems.