Acoustic cues to linguistic profiling? Machine learning of phonetic features of African American English

Abstract

Listeners can readily differentiate words spoken in an African American English (AAE) dialect from a Standard American English (SAE) dialect, even in absence of distinctive morphosyntactic features. However, it is still unclear what acoustic-phonetic cues listeners utilize to rapidly distinguish AAE from SAE. This study investigates the informativeness of various acoustic-phonetic cues to the characterization of AAE dialect. V and VC sequences (with C = /n/, /m/, /l/, /r/) from speech of 7 female speakers (4 SAE and 3 AAE), recorded during sociolinguistic interviews, were randomly selected and acoustically analyzed, controlling for coarticulatory context. Acoustic cues of F1, F2, F3, F4 formant trajectories, formant bandwidth, pitch variation, duration, intensity, and voice quality measures (e.g., harmonic-to-noise ratio, jitter, shimmer, and spectral slope) were measured in these segments to identify the extent of their contribution to separating AAE and SAE. The results from machine learning modeling of acoustic cues demonstrate that speech within an AAE dialect entails distinct acoustic characteristics and voice quality compared to SAE speech. These separate acoustic patterns between AAE and SAE dialect indicate the need for including dialect-specific acoustic cues both in automatic speech recognition applications and clinical assessments of speech-language disorders.