# Relationship between auditory and semantic entrainment using Deep Neural Networks (DNN)

arXiv: https://arxiv.org/abs/2312.16599 · HTML: https://mlsanity.com/paper/2312.16599/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 27 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Towards Learning a Universal Non-Semantic Representation of Speech](https://mlsanity.com/paper/2002.12764/) (2020)
- [SlovakBERT: Slovak Masked Language Model](https://mlsanity.com/paper/2109.15254/) (2021)
- [CoPaSul Manual -- Contour-based parametric and superpositional intonation stylization](https://mlsanity.com/paper/1612.04765/) (2016)

## Built on by (most cited first, 1 of 1)

- [A Cross-lingual Comparison of Human and Classification Model Entrainment Behavior in Code-switched Speech Settings](https://mlsanity.com/paper/2607.25202/) (2026)

## Abstract (arXiv)

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).