Categorization of U.S. regional dialects and race from speech

Abstract

Listeners extract a range of indexical information about speakers from the acoustic speech signal. Listeners show moderate to high accuracy with the two dimensions investigated here—race and regional dialect—with generally higher accuracy for race than regional dialect. In this project, both race and regional dialect identification from the same talkers were tested when the dimensions were varied orthogonally. In a forced-choice categorization task, listeners were presented with 144 unique audio clips of monolingual English talkers from Midland, New York City, and Southern dialect regions who were either Asian, Black, or white. Stimuli were presented to listeners in two separate, counter-balanced testing blocks (i.e., one for race and one for regional dialect). Results showed higher accuracy for race identification than regional dialect, with white Midland talkers most accurately identified by race and Southern Black talkers most accurately identified by dialect. Both tasks showed significant interactions between the two indexical dimensions. The results suggest that an irrelevant indexical dimension can impact perception of the relevant indexical dimension. Further, the composite of a listener's identity and their experiences with people of various ethnicities, racial and dialect groups can shape their perception and response biases. [Work supported by Indiana University Hutton Honors College.]