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Acoustic regularities in infant-directed speech and song across cultures

Nature Human Behaviour volume 6, pages 1545–1556 (2022)Cite this article

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Abstract

When interacting with infants, humans often alter their speech and song in ways thought to support communication. Theories of human child-rearing, informed by data on vocal signalling across species, predict that such alterations should appear globally. Here, we show acoustic differences between infant-directed and adult-directed vocalizations across cultures. We collected 1,615 recordings of infant- and adult-directed speech and song produced by 410 people in 21 urban, rural and small-scale societies. Infant-directedness was reliably classified from acoustic features only, with acoustic profiles of infant-directedness differing across language and music but in consistent fashions. We then studied listener sensitivity to these acoustic features. We played the recordings to 51,065 people from 187 countries, recruited via an English-language website, who guessed whether each vocalization was infant-directed. Their intuitions were more accurate than chance, predictable in part by common sets of acoustic features and robust to the effects of linguistic relatedness between vocalizer and listener. These findings inform hypotheses of the psychological functions and evolution of human communication.

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Fig. 1: Cross-cultural regularities in infant-directed vocalizations.
Fig. 2: How people alter their voices when vocalizing to infants.
Fig. 3: Naive listeners distinguish infant-directed vocalizations from adult-directed vocalizations across cultures.
Fig. 4: Human inferences about infant-directedness are predictable from acoustic features of vocalizations.

Data availability

The audio corpus is available at https://doi.org/10.5281/zenodo.5525161. All data, including supplementary fieldsite-level data and the recording collection protocol, are available at https://github.com/themusiclab/infant-speech-song and are permanently archived at https://doi.org/10.5281/zenodo.6562398. The preregistration for the auditory analyses is at https://osf.io/5r72u.

Code availability

Analysis and visualization code, a reproducible R Markdown manuscript and code for the naive listener experiment are available at https://github.com/themusiclab/infant-speech-song and are permanently archived at https://doi.org/10.5281/zenodo.6562398.

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Acknowledgements

This research was supported by the Harvard University Department of Psychology (M.M.K. and S.A.M.); the Harvard College Research Program (H.L.-R.); the Harvard Data Science Initiative (S.A.M.); the National Institutes of Health Director’s Early Independence Award DP5OD024566 (S.A.M. and C.B.H.); the Academy of Finland grant no. 298513 (J. Antfolk); the Royal Society of New Zealand Te Aparangi Rutherford Discovery Fellowship RDF-UOA1101 (Q.D.A. and T.A.V.); the Social Sciences and Humanities Research Council of Canada (L.K.C.); the Polish Ministry of Science and Higher Education grant no. N43/DBS/000068 (G.J.); the Fogarty International Center (P.M., A. Siddaiah and C.D.P.); the National Heart, Lung, and Blood Institute and the National Institute of Neurological Disorders and Stroke award no. D43 TW010540 (P.M. and A. Siddaiah); the National Institute of Allergy and Infectious Diseases award no. R15-AI128714-01 (P.M.); the Max Planck Institute for Evolutionary Anthropology (C.T.R. and C.M.); a British Academy Research Fellowship and grant no. SRG-171409 (G.D.S.); the Institute for Advanced Study in Toulouse, under an Agence nationale de la recherche grant, Investissements d’Avenir ANR-17-EURE-0010 (L.G. and J. Stieglitz); the Fondation Pierre Mercier pour la Science (C.S.); and the Natural Sciences and Engineering Research Council of Canada (S.E.T.). We thank the participants and their families for providing recordings; L. Sugiyama for supporting pilot data collection; J. Du, E. Pillsworth, P. Wiessner and J. Ziker for collecting or attempting to collect additional recordings; N. Nicolas for research assistance in the Republic of the Congo; Z. Jurewicz for research assistance in Toronto; M. Delfi and R. Sakaliou for research assistance in Indonesia; W. Naiou and A. Altrin for research assistance in Vanuatu; S. Atwood, A. Bergson, D. Li, L. Lopez and E. Radytė for project-wide research assistance; and J. Kominsky, L. Powell and L. Yurdum for feedback on the manuscript. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Courtney B. Hilton, Cody J. Moser.

Authors and Affiliations

  1. Department of Psychology, Harvard University, Cambridge, MA, USA

    Courtney B. Hilton, Cody J. Moser, Mila Bertolo, Harry Lee-Rubin, Constance M. Bainbridge, Jan Simson, Max M. Krasnow & Samuel A. Mehr

  2. Haskins Laboratories, Yale University, New Haven, CT, USA

    Courtney B. Hilton & Samuel A. Mehr

  3. Department of Cognitive and Information Sciences, University of California, Merced, Merced, CA, USA

    Cody J. Moser

  4. Boston College Department of Psychology, Chestnut Hill, MA, USA

    Dorsa Amir

  5. Department of Communication, University of California, Los Angeles, Los Angeles, CA, USA

    Constance M. Bainbridge

  6. Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands

    Jan Simson

  7. Operations, Information, and Decisions Department, The Wharton School of the University of Pennsylvania, Philadelphia, PA, USA

    Dean Knox

  8. Department of Anthropology, Boston University, Boston, MA, USA

    Luke Glowacki

  9. Jinka University, Jinka, Ethiopia

    Elias Alemu

  10. Department of Environmental Health, Faculty of Health Sciences, Jagiellonian University Medical College, Krakow, Poland

    Andrzej Galbarczyk & Grazyna Jasienska

  11. Department of Human Behavior, Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

    Cody T. Ross

  12. School of Psychology, Victoria University of Wellington, Wellington, New Zealand

    Mary Beth Neff & Alia Martin

  13. Department of Philosophy, Classics, History of Art and Ideas, University of Oslo, Oslo, Norway

    Mary Beth Neff

  14. Department of Psychology, University of Toronto, Scarborough, Toronto, Ontario, Canada

    Laura K. Cirelli

  15. Department of Psychology, University of Toronto, Mississauga, Mississauga, Ontario, Canada

    Laura K. Cirelli & Sandra E. Trehub

  16. Department of Mathematics, University of California, Los Angeles, Los Angeles, CA, USA

    Jinqi Song

  17. Department of Psychology, University of California, San Diego, La Jolla, CA, USA

    Minju Kim & Adena Schachner

  18. School of Psychology, University of Auckland, Auckland, New Zealand

    Tom A. Vardy & Quentin D. Atkinson

  19. Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

    Quentin D. Atkinson

  20. Department of Psychology, Åbo Akademi, Turku, Finland

    Amanda Salenius, Jannik Andelin & Jan Antfolk

  21. Department of Health Promotion Sciences, College of Public Health, University of Arizona, Tucson, AZ, USA

    Purnima Madhivanan

  22. Department of Medicine, Division of Infectious Diseases, College of Medicine, University of Arizona, Tucson, AZ, USA

    Purnima Madhivanan

  23. Department of Family & Community Medicine, College of Medicine, University of Arizona, Tucson, AZ, USA

    Purnima Madhivanan

  24. Public Health Research Institute of India, Mysuru, India

    Purnima Madhivanan & Anand Siddaiah

  25. Department of Anthropology, Ball State University, Muncie, IN, USA

    Caitlyn D. Placek

  26. Department of Anthropology, University College, London, London, UK

    Gul Deniz Salali & Sarai Keestra

  27. Amsterdam Reproduction & Development, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands

    Sarai Keestra

  28. Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA

    Manvir Singh

  29. Institute for Advanced Study in Toulouse, Toulouse, France

    Manvir Singh & Jonathan Stieglitz

  30. School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA

    Scott A. Collins

  31. Division of Anthropology, California State University, Fullerton, CA, USA

    John Q. Patton

  32. Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland

    Camila Scaff

  33. Université Toulouse, Toulouse, France

    Jonathan Stieglitz

  34. Universidad Nacional del Altiplano Puno, Puno, Peru

    Silvia Ccari Cutipa

  35. Department of Anthropology, University of California, Davis, Davis, CA, USA

    Cristina Moya

  36. Centre for Culture & Evolution, Brunel University, London, Uxbridge, UK

    Cristina Moya

  37. Future Generations University, Circle Ville, WV, USA

    Rohan R. Sagar

  38. Harpy Eagle Music Foundation, Georgetown, Guyana

    Rohan R. Sagar

  39. Mang’ola, Karatu, Tanzania

    Mariamu Anyawire

  40. Department of Archaeology and Heritage, University of Dar es Salaam, Dar es Salaam, Tanzania

    Audax Mabulla

  41. Department of Anthropology, University of California, Los Angeles, Los Angeles, CA, USA

    Brian M. Wood

  42. Division of Continuing Education, Harvard University, Cambridge, MA, USA

    Max M. Krasnow

  43. Data Science Initiative, Harvard University, Cambridge, MA, USA

    Samuel A. Mehr

Authors
  1. Courtney B. Hilton
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  2. Cody J. Moser
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  3. Mila Bertolo
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  4. Harry Lee-Rubin
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  5. Dorsa Amir
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  6. Constance M. Bainbridge
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  7. Jan Simson
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  8. Dean Knox
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  9. Luke Glowacki
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  10. Elias Alemu
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  11. Andrzej Galbarczyk
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  12. Grazyna Jasienska
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  13. Cody T. Ross
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  14. Mary Beth Neff
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  15. Alia Martin
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  16. Laura K. Cirelli
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  17. Sandra E. Trehub
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  18. Jinqi Song
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  19. Minju Kim
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  20. Adena Schachner
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  21. Tom A. Vardy
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  22. Quentin D. Atkinson
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  23. Amanda Salenius
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  24. Jannik Andelin
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  25. Jan Antfolk
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  26. Purnima Madhivanan
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  27. Anand Siddaiah
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  28. Caitlyn D. Placek
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  29. Gul Deniz Salali
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  30. Sarai Keestra
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  31. Manvir Singh
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  32. Scott A. Collins
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  36. Silvia Ccari Cutipa
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  37. Cristina Moya
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  38. Rohan R. Sagar
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  39. Mariamu Anyawire
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  40. Audax Mabulla
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  41. Brian M. Wood
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  42. Max M. Krasnow
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  43. Samuel A. Mehr
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Contributions

S.A.M. and M.M.K. conceived of the research, provided funding and coordinated the recruitment of collaborators and creation of the corpus. S.A.M. and M.M.K. designed the protocol for collecting vocalization recordings with input from D.A., who piloted it in the field. L.G., A.G., G.J., C.T.R., M.B.N., A. Martin, L.K.C., S.E.T., J. Song, M.K., A. Siddaiah, T.A.V., Q.D.A., J. Antfolk, P.M., A. Schachner, C.D.P., G.D.S., S.K., M.S., S.A.C., J.Q.P., C.S., J. Stieglitz, C.M., R.R.S. and B.M.W collected the field recordings, with support from E.A., A. Salenius, J. Andelin, S.C.C., M.A. and A. Mabulla. S.A.M., C.M.B. and J. Simson designed and implemented the online experiment. C.J.M. and H.L.-R. processed all recordings and designed the acoustic feature extraction with S.A.M. and M.M.K.; C.M.B. provided associated research assistance. C.M. designed the fieldsite questionnaire with assistance from M.B. and C.J.M., who collected the data from the principal investigators. C.B.H. and S.A.M. led analyses, with additional contributions from C.J.M., M.B., D.K. and M.M.K. C.B.H. and S.A.M. designed the figures. C.B.H. wrote computer code, with contributions from S.A.M., C.J.M. and M.B. D.K. conducted code review. C.J.M., H.L.-R., M.M.K. and S.A.M. wrote the initial manuscript. C.B.H. and S.A.M. wrote the first revision, with contributions from C.J.M. and M.B. S.A.M. wrote the second and third revisions, with contributions from C.B.H. and C.J.M.

Corresponding authors

Correspondence to Courtney B. Hilton, Cody J. Moser or Samuel A. Mehr.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Variation across societies of infant-directed alterations.

Estimated differences between infant-directed and adult-directed vocalizations, for acoustic feature, in each fieldsite (corresponding with the doughnut plots in Fig. 2). The estimates are derived from the random-effect components of the mixed-effects model reported in the main text. Cells of the table are shaded to facilitate the visibility of corpus-wide consistency (or inconsistency): redder cells represent features where infant-directed vocalizations have higher estimates than adult-directed vocalizations and bluer cells represent features with the reverse pattern. Within speech and song, acoustic features are ordered by their degree of cross-cultural regularity; some features showed the same direction of effect in all 21 societies (for example, for speech, median pitch and pitch variability), whereas others were more variable.

Extended Data Fig. 2 Principal-components analysis of acoustic features.

As an alternative approach to the acoustics data, we ran a principal-components analysis on the full 94 acoustic variables, to test whether an unsupervised method also yielded opposing trends in acoustic features across the different vocalization types. It did. The first three components explained 39% of total variability in the acoustic features. Moreover, the clearest differences between vocalization types accorded with the LASSO and mixed-effects modelling (Figs. 1b and 2). The first principal component most strongly differentiated speech and song, overall; the second most strongly differentiated infant-directed song from adult-directed song; and the third most strongly differentiated infant-directed speech from adult-directed speech. The violins indicate kernel density estimations and the boxplots represent the medians (centres), interquartile ranges (bounds of boxes) and 1.5 × IQR (whiskers). Significance values are computed via two-sided Wilcoxon signed-rank tests (n = 1,570 recordings); *p < 0.05, **p < 0.01, ***p < 0.001. Feature loadings are in Supplementary Table 7.

Extended Data Fig. 3 Screenshots from the naive listener experiment.

On each trial, participants heard a randomly selected vocalization from the corpus and were asked to quickly guess to whom the vocalization was directed: an adult or a baby. The experiment used large emoji and was designed to display comparably on desktop computers (a) or tablets/smartphones (b).

Extended Data Fig. 4 Response biases in the naive listener experiment.

a, Listeners showed reliable biases: regardless of whether a vocalization was infant- or adult-directed, the listeners gave speech recordings substantially fewer “baby” responses than expected by chance, and gave song recordings substantially more “baby” responses. The grey points represent average ratings for each of the recordings in the corpus that were used in the experiment (after exclusions, n = 1,138 recordings from the corpus of 1,615), split by speech and song; the orange and blue points indicate the means of each vocalization type; and the horizontal dashed line represents hypothetical chance level of 50%. b, Despite the response biases, within speech and song, the raw data nevertheless showed clear differences between infant-directed and adult-directed vocalizations, that is, by comparing infant-directedness scores within the same voice, across infant-directed and adult-directed vocalizations (visible here in the steep negative slopes of the grey lines). The main text results report only d’ statistics for these data, for simplicity, but the main effects are nonetheless visible here in the raw data. The points indicate average ratings for each recording; the grey lines connecting the points indicate the pairs of vocalizations produced by the same voice; the half-violins are kernel density estimations; the boxplots represent the medians, interquartile ranges and 95% confidence intervals (indicated by the notches); and the horizontal dashed lines indicate the response bias levels (from a).

Extended Data Fig. 5 Response-time analysis of naive listener experiment.

We recorded the response times of participants in their mobile or desktop browsers, using jsPsych (see Methods), and asked whether, when responding correctly, participants more rapidly detected infant-directedness in speech or song. They did not: a mixed-effects regression predicting the difference in response time between infant-directed and adult-directed vocalizations (within speech or song), adjusting hierarchically for fieldsite and world region, yielded no significant differences (ps > .05 from two-sided linear combination tests; no adjustments made for multiple comparisons). The grey points represent average ratings for each of the recordings in the corpus that were used in the experiment (after exclusions, n = 1,138 recordings from the corpus of 1,615), split by speech and song; the grey lines connecting the points indicate the pairs of vocalizations produced by the same participant; the half-violins are kernel density estimations; and the boxplots represent the medians, interquartile ranges and 95% confidence intervals (indicated by the notches).

Supplementary information

Supplementary Information

Supplementary Methods, Results, Figs. 1–4 and Tables 1–7.

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Hilton, C.B., Moser, C.J., Bertolo, M. et al. Acoustic regularities in infant-directed speech and song across cultures. Nat Hum Behav 6, 1545–1556 (2022). https://doi.org/10.1038/s41562-022-01410-x

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