A LANDMARK-BASED MODEL OF SPEECH PERCEPTION: HISTORY AND RECENT DEVELOPMENTS

This paper traces some of the history of the development of a model for speech perception in which words are assumed to be represented as sequences of bundles of binary distinctive features. In the model, probability estimates for feature values are derived from measurements of acoustic attributes in the vicinity of acoustic “landmarks.” Landmarks are detected based on amplitude changes in various energy bands, and landmarks or pairs of landmarks provide evidence for the existence of feature bundles. This paper discusses data and thought processes that prompted four significant changes in the formulation of the model over the past decade: rule-generated changes in segments versus modifications of cues for features, landmark detection, principles of cue selection, and the role of analysis-by-synthesis in verifying word hypotheses. The model currently allows for refinement of a cohort of words alongside the landmark-to-feature estimation process. In this view, initial estimates of some features based on local analysis of the signal are used to propose a cohort. The words in the cohort are used to inform landmark/feature estimation based on a more extended context. This process iterates between improving feature estimation and refining the cohort to arrive at the model output.