Spectro-temporal acoustical markers differentiate speech from song across cultures

Philippe Albouy (), Samuel A. Mehr, Roxane S. Hoyer, Jérémie Ginzburg, Yi Du and Robert J. Zatorre ()
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Philippe Albouy: Laval University
Samuel A. Mehr: Music and Sound Research (BRAMS)
Roxane S. Hoyer: Laval University
Jérémie Ginzburg: Laval University
Yi Du: Chinese Academy of Sciences
Robert J. Zatorre: Music and Sound Research (BRAMS)

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Humans produce two forms of cognitively complex vocalizations: speech and song. It is debated whether these differ based primarily on culturally specific, learned features, or if acoustical features can reliably distinguish them. We study the spectro-temporal modulation patterns of vocalizations produced by 369 people living in 21 urban, rural, and small-scale societies across six continents. Specific ranges of spectral and temporal modulations, overlapping within categories and across societies, significantly differentiate speech from song. Machine-learning classification shows that this effect is cross-culturally robust, vocalizations being reliably classified solely from their spectro-temporal features across all 21 societies. Listeners unfamiliar with the cultures classify these vocalizations using similar spectro-temporal cues as the machine learning algorithm. Finally, spectro-temporal features are better able to discriminate song from speech than a broad range of other acoustical variables, suggesting that spectro-temporal modulation—a key feature of auditory neuronal tuning—accounts for a fundamental difference between these categories.

Date: 2024
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DOI: 10.1038/s41467-024-49040-3

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