Author
Listed:
- Jeffrey A. Brooks
(Research Division, Hume AI
University of California, Berkeley)
- Panagiotis Tzirakis
(Research Division, Hume AI)
- Alice Baird
(Research Division, Hume AI)
- Lauren Kim
(Research Division, Hume AI)
- Michael Opara
(Research Division, Hume AI)
- Xia Fang
(Zhejiang University)
- Dacher Keltner
(Research Division, Hume AI
University of California, Berkeley)
- Maria Monroy
(University of California, Berkeley)
- Rebecca Corona
(University of California, Berkeley)
- Jacob Metrick
(Research Division, Hume AI)
- Alan S. Cowen
(Research Division, Hume AI
University of California, Berkeley)
Abstract
Human social life is rich with sighs, chuckles, shrieks and other emotional vocalizations, called ‘vocal bursts’. Nevertheless, the meaning of vocal bursts across cultures is only beginning to be understood. Here, we combined large-scale experimental data collection with deep learning to reveal the shared and culture-specific meanings of vocal bursts. A total of n = 4,031 participants in China, India, South Africa, the USA and Venezuela mimicked vocal bursts drawn from 2,756 seed recordings. Participants also judged the emotional meaning of each vocal burst. A deep neural network tasked with predicting the culture-specific meanings people attributed to vocal bursts while disregarding context and speaker identity discovered 24 acoustic dimensions, or kinds, of vocal expression with distinct emotion-related meanings. The meanings attributed to these complex vocal modulations were 79% preserved across the five countries and three languages. These results reveal the underlying dimensions of human emotional vocalization in remarkable detail.
Suggested Citation
Jeffrey A. Brooks & Panagiotis Tzirakis & Alice Baird & Lauren Kim & Michael Opara & Xia Fang & Dacher Keltner & Maria Monroy & Rebecca Corona & Jacob Metrick & Alan S. Cowen, 2023.
"Deep learning reveals what vocal bursts express in different cultures,"
Nature Human Behaviour, Nature, vol. 7(2), pages 240-250, February.
Handle:
RePEc:nat:nathum:v:7:y:2023:i:2:d:10.1038_s41562-022-01489-2
DOI: 10.1038/s41562-022-01489-2
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