A Data-Driven Method for Analyzing and Quantifying Lyrics-Dance Motion Relationships

Kento Watanabe, Masataka Goto


Abstract
Dancing to music with lyrics is a popular form of expression. While it is generally accepted that there are relationships between lyrics and dance motions, previous studies have not explored these relationships. A major challenge is that the relationships between lyrics and dance motions are not constant throughout a song but are instead localized to specific parts. To address this challenge, we hypothesize that lyrics and dance motions that co-occur across multiple songs are related. Based on this hypothesis, we propose a novel data-driven method to detect the parts of songs where meaningful relationships between lyrics and dance motions exist. We use clustering to transform lyrics and dance motions into symbols, enabling the calculation of co-occurrence frequencies and detection of significant correlations. The effectiveness of our method is validated by a dataset of time-synchronized lyrics and dance motions, which showed high correlation values for emotionally salient lyrics such as “love”, which is expressed in heart-shaped motions. Furthermore, using our relationship detection method, we propose a method for retrieving dance motions from lyrics that outperforms previous text-to-motion retrieval methods, which focus on prose and non-dance motions.
Anthology ID:
2025.naacl-long.401
Volume:
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
April
Year:
2025
Address:
Albuquerque, New Mexico
Editors:
Luis Chiruzzo, Alan Ritter, Lu Wang
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7901–7916
Language:
URL:
https://preview.aclanthology.org/moar-dois/2025.naacl-long.401/
DOI:
10.18653/v1/2025.naacl-long.401
Bibkey:
Cite (ACL):
Kento Watanabe and Masataka Goto. 2025. A Data-Driven Method for Analyzing and Quantifying Lyrics-Dance Motion Relationships. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7901–7916, Albuquerque, New Mexico. Association for Computational Linguistics.
Cite (Informal):
A Data-Driven Method for Analyzing and Quantifying Lyrics-Dance Motion Relationships (Watanabe & Goto, NAACL 2025)
Copy Citation:
PDF:
https://preview.aclanthology.org/moar-dois/2025.naacl-long.401.pdf