A Comparative Cross Language View On Acted Databases Portraying Basic Emotions Utilising Machine Learning
Felix Burkhardt, Anabell Hacker, Uwe Reichel, Hagen Wierstorf, Florian Eyben, Björn Schuller
Abstract
Since several decades emotional databases have been recorded by various laboratories. Many of them contain acted portrays of Darwin’s famous “big four” basic emotions. In this paper, we investigate in how far a selection of them are comparable by two approaches: on the one hand modeling similarity as performance in cross database machine learning experiments and on the other by analyzing a manually picked set of four acoustic features that represent different phonetic areas. It is interesting to see in how far specific databases (we added a synthetic one) perform well as a training set for others while some do not. Generally speaking, we found indications for both similarity as well as specificiality across languages.- Anthology ID:
- 2022.lrec-1.204
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 1917–1924
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2022.lrec-1.204/
- DOI:
- Cite (ACL):
- Felix Burkhardt, Anabell Hacker, Uwe Reichel, Hagen Wierstorf, Florian Eyben, and Björn Schuller. 2022. A Comparative Cross Language View On Acted Databases Portraying Basic Emotions Utilising Machine Learning. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1917–1924, Marseille, France. European Language Resources Association.
- Cite (Informal):
- A Comparative Cross Language View On Acted Databases Portraying Basic Emotions Utilising Machine Learning (Burkhardt et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2022.lrec-1.204.pdf