  Speakers tend to engage in adaptive behavior, known as entrainment, when they
become similar to their interlocutor in various aspects of speaking. We present
an unsupervised deep learning framework that derives meaningful representation
from textual features for developing semantic entrainment. We investigate the
model's performance by extracting features using different variations of the
BERT model (DistilBERT and XLM-RoBERTa) and Google's universal sentence encoder
(USE) embeddings on two human-human (HH) corpora (The Fisher Corpus English
Part 1, Columbia games corpus) and one human-machine (HM) corpus (Voice
Assistant Conversation Corpus (VACC)). In addition to semantic features we also
trained DNN-based models utilizing two auditory embeddings (TRIpLet Loss
network (TRILL) vectors, Low-level descriptors (LLD) features) and two units of
analysis (Inter pausal unit and Turn). The results show that semantic
entrainment can be assessed with our model, that models can distinguish between
HH and HM interactions and that the two units of analysis for extracting
acoustic features provide comparable findings.
