  In this paper, we explore automatic prediction of dialect density of the
African American English (AAE) dialect, where dialect density is defined as the
percentage of words in an utterance that contain characteristics of the
non-standard dialect. We investigate several acoustic and language modeling
features, including the commonly used X-vector representation and ComParE
feature set, in addition to information extracted from ASR transcripts of the
audio files and prosodic information. To address issues of limited labeled
data, we use a weakly supervised model to project prosodic and X-vector
features into low-dimensional task-relevant representations. An XGBoost model
is then used to predict the speaker's dialect density from these features and
show which are most significant during inference. We evaluate the utility of
these features both alone and in combination for the given task. This work,
which does not rely on hand-labeled transcripts, is performed on audio segments
from the CORAAL database. We show a significant correlation between our
predicted and ground truth dialect density measures for AAE speech in this
database and propose this work as a tool for explaining and mitigating bias in
speech technology.
