Relationship between auditory and semantic entrainment using Deep Neural
  Networks (DNN)
Jay Kejriwal Štefan Beňuš
The tendency of people to engage in similar, matching, or synchronized
behaviour when interacting is known as entrainment. Many studies examined
linguistic (syntactic and lexical structures) and paralinguistic (pitch,
intensity) entrainment, but less attention was given to finding the
relationship between them. In this study, we utilized state-of-the-art DNN
embeddings such as BERT and TRIpLet Loss network (TRILL) vectors to extract
features for measuring semantic and auditory similarities of turns within
dialogues in two comparable spoken corpora of two different languages. We found
people's tendency to entrain on semantic features more when compared to
auditory features. Additionally, we found that entrainment in semantic and
auditory linguistic features are positively correlated. The findings of this
study might assist in implementing the mechanism of entrainment in
human-machine interaction (HMI).