  Entrainment is a known adaptation mechanism that causes interaction
participants to adapt or synchronize their acoustic characteristics.
Understanding how interlocutors tend to adapt to each other's speaking style
through entrainment involves measuring a range of acoustic features and
comparing those via multiple signal comparison methods. In this work, we
present a turn-level distance measure obtained in an unsupervised manner using
a Deep Neural Network (DNN) model, which we call Neural Entrainment Distance
(NED). This metric establishes a framework that learns an embedding from the
population-wide entrainment in an unlabeled training corpus. We use the
framework for a set of acoustic features and validate the measure
experimentally by showing its efficacy in distinguishing real conversations
from fake ones created by randomly shuffling speaker turns. Moreover, we show
real world evidence of the validity of the proposed measure. We find that high
value of NED is associated with high ratings of emotional bond in suicide
assessment interviews, which is consistent with prior studies.
