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Synchronization in Interpersonal Speech.

Shahin Amiriparian¹, Jing Han¹, Maximilian Schmitt¹

¹ZD.B Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany.

Frontiers in Robotics and AI

|January 27, 2021

Summary

This summary is machine-generated.

Individuals unconsciously imitate partners during conversations, improving relationships. This study introduces novel computational methods to automatically detect speech synchrony across cultures, revealing variations in interaction dynamics.

Keywords:

autoencoders computational paralinguistics human-human interaction machine learning speech processing speech synchronization

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Area of Science:

Computational linguistics
Social psychology
Speech processing

Background:

Interpersonal synchrony, often unconscious imitation, enhances communication and relationships.
Existing methods for automatically detecting synchrony in speech are limited.
Previous work utilized acoustic descriptors and autoencoders (AEs) for synchrony analysis.

Purpose of the Study:

To develop and evaluate novel computational approaches for recognizing speech synchrony.
To investigate the influence of culture on interpersonal synchrony.
To compare acoustic and linguistic methods for synchrony detection.

Main Methods:

Utilized a database of 394 speakers from six cultures.
Implemented autoencoders (AEs) trained on individual speakers' acoustic features.
Tested speaker features against partner-trained AEs and employed Deep Spectrum toolkit for deep audio representations.
Compared acoustic reconstruction errors with linguistic analyses (word counts, word2vec embeddings).

Main Results:

Demonstrated a degree of synchrony in all analyzed interactions.
Found significant variations in synchrony levels across the six investigated cultures.
Acoustic analysis using AEs and Deep Spectrum features corroborated linguistic findings.

Conclusions:

Automatic detection of speech synchrony is feasible using advanced computational methods.
Cultural background significantly influences the degree of interpersonal synchrony.
Combining acoustic and linguistic features provides a robust approach to understanding interaction dynamics.