Infant cries convey both stable and dynamic information about age and identity.


Journal

Communications psychology
ISSN: 2731-9121
Titre abrégé: Commun Psychol
Pays: England
ID NLM: 9918716686206676

Informations de publication

Date de publication:
02 Oct 2023
Historique:
received: 23 11 2022
accepted: 31 08 2023
medline: 7 9 2024
pubmed: 7 9 2024
entrez: 6 9 2024
Statut: epublish

Résumé

What information is encoded in the cries of human babies? While it is widely recognized that cries can encode distress levels, whether cries reliably encode the cause of crying remains disputed. Here, we collected 39201 cries from 24 babies recorded in their homes longitudinally, from 15 days to 3.5 months of age, a database we share publicly for reuse. Based on the parental action that stopped the crying, which matched the parental evaluation of cry cause in 75% of cases, each cry was classified as caused by discomfort, hunger, or isolation. Our analyses show that baby cries provide reliable information about age and identity. Baby voices become more tonal and less shrill with age, while individual acoustic signatures drift throughout the first months of life. In contrast, neither machine learning algorithms nor trained adult listeners can reliably recognize the causes of crying.

Identifiants

pubmed: 39242685
doi: 10.1038/s44271-023-00022-z
pii: 10.1038/s44271-023-00022-z
doi:

Types de publication

Journal Article

Langues

eng

Pagination

26

Informations de copyright

© 2023. The Author(s).

Références

Barr, R. G., Hopkins, B., & Green, J. A. Crying as a Sign, a Symptom, and a Signal (Mac Keith Press, 2000).
Lingle, S., Wyman, M. T., Kotrba, R., Teichroeb, L. J. & Romanow, C. A. What makes a cry a cry? A review of infant distress vocalizations. Curr. Zool. 58, 698–726 (2012).
Mathevon, N. The Voices of Nature: How and Why Animals Communicate (Princeton University Press, 2023).
Soltis, J. The signal functions of early infant crying. Behav. Brain Sci. 27, 443–458 (2004).
pubmed: 15773426
Zeifman, D. M. An ethological analysis of human infant crying: answering Tinbergen’s four questions. Dev. Psychobiol. 39, 265–285 (2001).
pubmed: 11745323
Koutseff, A., Reby, D., Martin, O., Levrero, F., Patural, H. & Mathevon, N. The acoustic space of pain: cries as indicators of distress recovering dynamics in pre-verbal infants. Bioacoustics 27, 313–325 (2018).
Dudek, J., Faress, A., Bornstein, M. H. & Haley, D. W. Infant cries rattle adult cognition. PloS One 11, e0154283 (2016).
pubmed: 27191845 pmcid: 4871531
Marler, P. Social organization, communication and graded signals: the chimpanzee and the gorilla. in Growing Points Ethology, Vol. 239 (eds Bateson, P. P. G. & Hinde, R. A.) (Cambridge University Press, 1976).
Pisanski, K., Bryant, G. A., Cornec, C., Anikin, A. & Reby, D. Form follows function in human nonverbal vocalisations. Ethol. Ecol. Evol. 34, 303–321 (2022).
Bell, S. M. & Ainsworth, M. D. S. Infant crying and maternal responsiveness. Child Dev. 43, 1171–1190 (1972).
pubmed: 4643768
Zeifman, D. M. Acoustic features of infant crying related to intended caregiving intervention. Infant Child Dev. 13, 111–122 (2004).
Gustafson, G. E., Green, J. A. & Cleland, J. W. Robustness of individual identity in the cries of human infants. Dev. Psychobiol. 27, 1–9 (1994).
pubmed: 8112484
Müller, E., Hollien, H. & Murry, T. Perceptual responses to infant crying: Identification of cry types. J. Child Infant 1, 89–95 (1974).
Wasz-Höckert, O., Partanen, T., Vuorenkoski, V., Michelsson, K. & Valanne, E. The identification of some specific meanings in infant vocalization. Experientia 20, 154–154 (1964).
pubmed: 4159066
Wiesenfeld, A. R., Malatesta, C. Z. & Deloach, L. L. Differential parental response to familiar and unfamiliar infant distress signals. Infant Behav. Dev. 4, 281–295 (1981).
Gustafson, G. E. & Harris, K. L. Women’s responses to young infants’ cries. Dev. Psychol. 26, 144–152 (1990).
Corvin, S., Fauchon, C., Peyron, R., Reby, D. & Mathevon, N. Adults learn to identify pain in babies’ cries. Curr. Biol. 32, R824–R825 (2022).
pubmed: 35944479
Osmani, A., Hamidi, M., & Chibani, A. Machine learning approach for infant cry interpretation. In Proc. International Conference on Tools with Artificial Intelligence https://doi.org/10.1109/ICTAI.2017.00038 (2017).
Jeyaraman, S., Muthusamy, H., Khairunizam, W., Jeyaraman, S., Nadarajaw, T., Yaacob, S. & Nisha, S. A review: survey on automatic infant cry analysis and classification. Health Technol. 8, 391–404 (2018).
Ji, C., Mudiyanselage, T. B., Gao, Y. & Pan, Y. A review of infant cry analysis and classification. EURASIP J. Audio Speech Music Process. 2021, 1–17 (2021).
Felipe, G. Z., Aguiar, R. L., Costa, Y. M. G., Silla Jr. C. N., Brahnam, S., Nanni, L., & McMurtrey, S. Identification of infants’ cry motivation using spectrograms. In Proc. International Conference on Systems, Signals and Image Processing (IWSSIP) https://doi.org/10.1109/iwssip.2019.878731 (2019).
Mittal, V. K. Discriminating the infant cry sounds due to pain vs. discomfort towards assisted clinical diagnosis. In Proc. Workshop on Speech and Language Processing for Assistive Technologies (SLPAT), 37–42 (ISCA, 2016).
Chang, C-Y., & Li J-J. Application of deep learning for recognizing infant cries. In Proc. International Conference on Consumer Electronics – Taiwan (IEEE, 2016).
Tsakalidou, V. N. Infant crying patterns’ analysis using machine learning. In Proc. 4th International Conference on Communication, Computing and Electronics Systems (Bindhu, V. et al., eds), Lecture Notes in Electrical Engineering 977, https://doi.org/10.1007/978-981-19-7753-4_51 (2023).
Veres, G. (2020). Donate a Cry - Corpus. https://github.com/gveres/donateacry-corpus .
Sharma, K., Gupta, C., & Gupta, S. Infant weeping calls decoder using statistical feature extraction and Gaussian mixture models. In: Proc 10th International Conference On Computing Communication and Networking Technologies (ICCCNT). 1–6 (IEEE, 2019). https://doi.org/10.1109/ICCCNT45670.2019.8944527 .
Rezaee, K., Zadeh, H. G., Qi, L., Rabiee, H., & Khosravi, M. R. Can you understand what I am crying? A decision-making system for classifying infants’ cry languages based on deepSVM Model. ACM Trans Asian Low-Resour. Lang. Inf. Process. https://doi.org/10.1145/3579032 (2023).
Smith, J. Calm the crying: using the Dunstan baby language, the secret baby language that reveals the hidden meaning behind an infant’s cry. Libr J 240 (2013).
Banica, I. A., Cucu, H., Buzo, A., Burileanu, D., & Burileanu, C. Automatic methods for infant cry classification. In: Proc International Conference on Communications (COMM), Bucharest, Romania,. 51–54 (IEEE Press, 2016) https://doi.org/10.1109/ICComm.2016.7528261 .
Maghfira, T. N., Basaruddin, T. & Krisnadhi, A. Infant cry classification using CNN—RNN. 4th International Seminar on Sensors, Instrumentation, Measurement and Metrology. 1528, 012019 (2020).
Bano, S. & RaviKumar, K. M. Decoding baby talk: Basic approach for normal classification of infant cry signal. International Journal of Computer Applications (0975-8887), In Proc. International Conference on Current Trends in Advanced Computing (ICCTAC-2015), 26–24 (ICCTAC-2015, 2015).
Franti E., Ispas, I., & Dascalu, M. Testing the universal baby language hypothesis—automatic infant speech recognition with CNNs. In 41st International Conference on Telecommunications and Signal Processing (TSP). 1–4. https://doi.org/10.1109/TSP.2018.8441412 (IEEE, 2018).
Gustafson, G. E., Wood, R. M., & Green, J. A. Can we hear the causes of infants’ crying? in Cry as a Sign, a Symptom, and a Signal. Clinical, Emotional and Developmental Aspects of Infant and Toddler Crying (eds Barr, R. G., Hopkins, B. & Green, J. A.) (MacKeith Press, 2000).
Mende, W., Herzel, H. & Wermke, K. Bifurcations and chaos in newborn infant cries. Phys. Lett. A 145, 418–424 (1990).
Fitch, W. T., Neubauer, J. & Herzel, H. Calls out of chaos: the adaptive significance of nonlinear phenomena in mammalian vocal production. Anim. Behav. 63, 407–418 (2002).
Bellieni, C. V., Sisto, R., Cordelli, D. M. & Buonocore, G. Cry features reflect pain intensity in term newborns: an alarm threshold. Pediatr. Res. 55, 142–146 (2004).
pubmed: 14605260
Leger, D. W., Thompson, R. A., Merritt, J. A. & Benz, J. J. Adult perception of emotion intensity in human infant cries: effects of infant age and cry acoustics. Child Dev. 67, 3238–3249 (1996).
pubmed: 9071779
Gustafson, G. E., Sanborn, S. M., Lin, H. & Green, J. A. Newborns’ cries are unique to individuals (but not to language environment). Infancy 22, 736–747 (2017).
Bouchet, H., Plat, A., Levréro, F., Reby, D., Patural, H. & Mathevon, N. Baby cry recognition is independent of motherhood but improved by experience and exposure. Proc. R. Soc. B 287, 20192499 (2020).
pubmed: 32070250 pmcid: 7062011
Gustafsson, E., Levréro, F., Reby, D. & Mathevon, N. Fathers are just as good as mothers at recognizing the cries of their baby. Nat. Commun. 4, 1698 (2013).
pubmed: 23591865
Pisanski, K., Raine, J. & Reby, D. Individual differences in human voice pitch are preserved from speech to screams, roars, and pain cries. R. Soc. Open Sci. 7, 191642 (2020).
pubmed: 32257325 pmcid: 7062086
Reby, D., Levréro, F., Gustafsson, E. & Mathevon, N. Sex stereotypes influence adults’ perception of babies’ cries. BMC Psychol. 4, 19 (2016).
pubmed: 27079192 pmcid: 4832517
Levréro, F., Mathevon, N., Pisanski, K., Gustafsson, E. & Reby, D. The pitch of babies’ cries predicts their voice pitch at age 5. Biol. Lett. 14, 20180065 (2018).
pubmed: 29997184 pmcid: 6083235
Fouquet, M., Pisanski, K., Mathevon, N. & Reby, D. Seven and up: Individual differences in male voice fundamental frequency emerge before puberty and remain stable throughout adulthood. R. Soc. Open Sci. 3, 160395 (2016).
pubmed: 27853555 pmcid: 5098980
Titze, I. R. Physiologic and acoustic differences between male and female voices. J. Acoust. Soc. Am. 85, 1699–1707 (1989).
pubmed: 2708686
Wermke, K., Quast, A. & Hesse, V. From melody to words: the role of sex hormones in early language development. Horm. Behav. 104, 206–215 (2018).
pubmed: 29573996
Boersma, P., & Weenink, D. Praat: Doing phonetics by computer (6.1.50) (2021).
Anikin, A. Soundgen: an open-source tool for synthesizing nonverbal vocalizations. Behav. Res. Methods 51, 778–792 (2019).
pubmed: 30054898
Charlton, B. D., Pisanski, K., Raine, J., & Reby, D. Coding of static information in terrestrial mammal vocal signals. in Animal Signals and Communication (eds Aubin, T. & Mathevon, N.) 115–136 (Springer Nature, 2020).
Kreiman, J., & Sidtis, D. Foundations of voice studies: An interdisciplinary approach to voice production and perception. (Wiley-Blackwell, 2011).
Pisanski, K., & Bryant, G. A. The evolution of voice perception. in The Oxford Handbook of Voice Studies (eds Eidsheim, N. S & Meizel, K. L.) (Oxford University Press, 2019).
Arias, P., Rachman, L., Liuni, M. & Aucouturier, J. J. Beyond correlation: acoustic transformation methods for the experimental study of emotional voice and speech. Emot. Rev. 13, 12–24 (2021).
Bürkner, P.-C. brms: An R package for Bayesian generalized linear mixed models using Stan. J. Stat. Softw. 80, 1–23 (2016).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Melville, J., Lun, A. & Djekidel, M. uwot: The uniform manifold approximation and projection (UMAP) method for dimensionality reduction. R; Package Version, 15 https://CRAN.R-project.org/package=uwot (2020).
Sainburg, T., Thielk, M. & Gentner, T. Q. Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires. PLoS Comput. Biol. 16, e1008228 (2020).
pubmed: 33057332 pmcid: 7591061
Palan, S. & Schitter, C. Prolific. Ac—A subject pool for online experiments. J. Behav. Exp. Financ. 17, 22–27 (2018).
Finger, H., Goeke, C., Diekamp, D., Standvoß, K., & König, P. LabVanced: A unified JavaScript framework for online studies. In Proc. International conference on computational social science (Cologne) (2017).
Watanabe, S. & Opper, M. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594 (2010).
Busby, P. A. & Plant, G. L. Formant frequency values of vowels produced by preadolescent boys and girls. J. Acoust. Soc. Am. 97, 2603–2606 (1995).
pubmed: 7714275
Cartei, V., Bond, R. & Reby, D. What makes a voice masculine: physiological and acoustical correlates of women’s ratings of men’s vocal masculinity. Horm. Behav. 66, 569–576 (2014).
pubmed: 25169905
Lee, S., Potamianos, A. & Narayanan, S. Acoustics of children’s speech: developmental changes of temporal and spectral parameters. J. Acoust. Soc. Am. 105, 1455–1468 (1999).
pubmed: 10089598
Sussman, J. E. & Sapienza, C. Articulatory, developmental, and gender effects on measures of fundamental frequency and jitter. J. Voice 8, 145–156 (1994).
pubmed: 8061770
Fuamenya, N. A., Robb, M. P. & Wermke, K. Noisy but effective: crying across the first 3 months of life. J. Voice 29, 281–286 (2015).
pubmed: 25484260
Hartnick, C. J., Rehbar, R. & Prasad, V. Development and maturation of the pediatric human vocal fold lamina propria. Laryngoscope 115, 4–15 (2005).
pubmed: 15630357
Murray, A. D. Infant crying as an elicitor of parental behavior: an examination of two models. Psychol. Bull. 86, 191 (1979).
pubmed: 377352
Wermke, K., Robb, M. P. & Schluter, P. J. Melody complexity of infants’ cry and non-cry vocalisations increases across the first six months. Sci. Rep. 11, 1–11 (2021).
Scheiner, E., Hammerschmidt, K., Jürgens, U. & Zwirner, P. Acoustic analyses of developmental changes and emotional expression in the preverbal vocalizations of infants. J. Voice 16, 509–529 (2002).
pubmed: 12512639
Rothgänger, H. Analysis of the sounds of the child in the first year of age and a comparison to the language. Early Hum. Dev. 75, 55–69 (2003).
pubmed: 14652159
Sauvé, C. C., Beauplet, G., Hammill, M. O. & Charrier, I. Acoustic analysis of airborne, underwater, and amphibious mother attraction calls by wild harbor seal pups (Phoca vitulina). J. Mammal. 96, 591–602 (2015).
Cornec, C., Hingrat, Y., Planas-Bielsa, V., Abi Hussein, H. & Rybak, F. Individuality in houbara chick calls and its dynamics throughout ontogeny. Endanger. Species Res. 47, 61–73 (2022).
Baeck, H. E. & de Souza, M. N. Longitudinal study of the fundamental frequency of hunger cries along the first 6 months of healthy babies. J. Voice 21, 551–559 (2007).
pubmed: 16730945
Gabrieli, G., Scapin, G., Bornstein, M. H., & Esposito, G. Are cry studies replicable? An analysis of participants, procedures, and methods adopted and reported in studies of infant cries. Acoustics 1, 866–883 (2019).
Gilbert, H. R. & Robb, M. P. Vocal fundamental frequency characteristics of infant hunger cries: Birth to 12 months. Int. J. Pediatr. Otorhinolaryngol. 34, 237–243 (1996).
pubmed: 8839074
Lind, K. & Wermke, K. Development of the vocal fundamental frequency of spontaneous cries during the first 3 months. Int. J. Pediatr. Otorhinolaryngol. 64, 97–104 (2002).
pubmed: 12049822
Wermke, K., Mende, W., Manfredi, C. & Bruscaglioni, P. Developmental aspects of infant’s cry melody and formants. Med. Eng. Phys. 24, 501–514 (2002).
pubmed: 12237046
Wolke, D., Bilgin, A. & Samara, M. Systematic review and meta-analysis: fussing and crying durations and prevalence of colic in infants. J. Pediatr. 185, 55–61 (2017).
pubmed: 28385295
Konner, M. The evolution of childhood: Relationships, Emotion, Mind. (Harvard University Press, 2011).
Hewlett, B. S. Hunter-gatherer childhoods: Evolutionary, developmental, and cultural perspectives (Routledge, 2017).
Hirschberg, J., Szende, T., Koltai, P., & Illényi, A. Pediatric Airway: Cry, Stridor, and Cough. (Plural Publishing, 2009).
Gladding, S. T. Effects of training versus non-training in identification of infant cry-signals: a longitudinal study. Percept. Mot. Skills 48, 752–754 (1979).
pubmed: 482025
Dezecache, G., Zuberbühler, K., Davila-Ross, M. & Dahl, C. D. Flexibility in wild infant chimpanzee vocal behavior. J. Lang. Evol. 6, 37–53 (2021).
Levréro, F. & Mathevon, N. Vocal signature in wild infant chimpanzees. Am. J. Primatol. 75, 324–332 (2013).
pubmed: 23229622
Keenan, S., Mathevon, N., Stevens, J. M., Nicolè, F., Zuberbühler, K., Guéry, J.-P. & Levréro, F. The reliability of individual vocal signature varies across the bonobo’s graded repertoire. Anim. Behav.169, 9–21 (2020).
Wright, J., & Leonard, M. L. The evolution of Begging: Competition, Cooperation and Communication. (Springer Science & Business Media, 2007).

Auteurs

Marguerite Lockhart-Bouron (M)

Neonatal and Pediatric Intensive Care Unit, SAINBIOSE Iaboratory, Inserm, University Hospital of Saint-Etienne, University of Saint-Etienne, Saint-Etienne, France.

Andrey Anikin (A)

ENES Bioacoustics Research Laboratory, CRNL, CNRS, Inserm, University of Saint-Etienne, Saint-Etienne, France.
Division of Cognitive Science, Lund University, Lund, Sweden.

Katarzyna Pisanski (K)

ENES Bioacoustics Research Laboratory, CRNL, CNRS, Inserm, University of Saint-Etienne, Saint-Etienne, France.
Laboratoire Dynamique du Langage DDL, CNRS, University of Lyon 2, Lyon, France.

Siloé Corvin (S)

ENES Bioacoustics Research Laboratory, CRNL, CNRS, Inserm, University of Saint-Etienne, Saint-Etienne, France.
Central Integration of Pain-Neuropain Laboratory, CRNL, CNRS, Inserm, UCB Lyon 1, University of Saint-Etienne, Saint-Etienne, France.

Clément Cornec (C)

ENES Bioacoustics Research Laboratory, CRNL, CNRS, Inserm, University of Saint-Etienne, Saint-Etienne, France.

Léo Papet (L)

ENES Bioacoustics Research Laboratory, CRNL, CNRS, Inserm, University of Saint-Etienne, Saint-Etienne, France.

Florence Levréro (F)

ENES Bioacoustics Research Laboratory, CRNL, CNRS, Inserm, University of Saint-Etienne, Saint-Etienne, France.
Institut universitaire de France, Paris, France.

Camille Fauchon (C)

Central Integration of Pain-Neuropain Laboratory, CRNL, CNRS, Inserm, UCB Lyon 1, University of Saint-Etienne, Saint-Etienne, France.

Hugues Patural (H)

Neonatal and Pediatric Intensive Care Unit, SAINBIOSE Iaboratory, Inserm, University Hospital of Saint-Etienne, University of Saint-Etienne, Saint-Etienne, France.

David Reby (D)

ENES Bioacoustics Research Laboratory, CRNL, CNRS, Inserm, University of Saint-Etienne, Saint-Etienne, France.
Institut universitaire de France, Paris, France.

Nicolas Mathevon (N)

ENES Bioacoustics Research Laboratory, CRNL, CNRS, Inserm, University of Saint-Etienne, Saint-Etienne, France. mathevon@univ-st-etienne.fr.
Institut universitaire de France, Paris, France. mathevon@univ-st-etienne.fr.
Ecole Pratique des Hautes Etudes, CHArt Lab, PSL Research University, Paris, France. mathevon@univ-st-etienne.fr.

Classifications MeSH