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).
Cite as: Kejriwal, J., Beňuš, Š. (2023) Relationship between auditory and semantic entrainment using Deep Neural Networks (DNN). Proc. INTERSPEECH 2023, 2623-2627, doi: 10.21437/Interspeech.2023-1947
@inproceedings{kejriwal23b_interspeech,
author={Jay Kejriwal and Štefan Beňuš},
title={{Relationship between auditory and semantic entrainment using Deep Neural Networks (DNN)}},
year=2023,
booktitle={Proc. INTERSPEECH 2023},
pages={2623--2627},
doi={10.21437/Interspeech.2023-1947},
issn={2308-457X}
}