  Human interlocutors tend to engage in adaptive behavior known as entrainment
to become more similar to each other. Isolating the effect of consistency,
i.e., speakers adhering to their individual styles, is a critical part of the
analysis of entrainment. We propose to treat speakers' initial vocal features
as confounds for the prediction of subsequent outputs. Using two existing
neural approaches to deconfounding, we define new measures of entrainment that
control for consistency. These successfully discriminate real interactions from
fake ones. Interestingly, our stricter methods correlate with social variables
in opposite direction from previous measures that do not account for
consistency. These results demonstrate the advantages of using neural networks
to model entrainment, and raise questions regarding how to interpret prior
associations of conversation quality with entrainment measures that do not
account for consistency.
