Was That a Scream? Listener Agreement and Major Distinguishing Acoustic Features
Human screams have been suggested to comprise a salient and readily identified call type, yet few studies have explored the degree to which people agree on what constitutes a scream, and the defining acoustic structure of screams has not been fully determined. In this study, participants listened to 75 human vocal sounds, representing both a broad acoustical range and array of emotional contexts, and classified each as to whether it was a scream or not. Participants showed substantial agreement on which sounds were considered screams, consistent with the idea of screams as a basic call type. Agreement on classifications was related to participant gender, emotion processing accuracy, and empathy. To characterize the acoustic structure of screams, we measured the stimuli on 27 acoustic parameters. Principal components analysis and generalized linear mixed modeling indicated that classification as a scream was positively correlated with 3 acoustic dimensions: one corresponding to high pitch and roughness, another corresponding to wide fundamental frequency variability and narrow interquartile range bandwidth, and a third positively correlated with peak frequency slope. Twenty-six stimuli were agreed upon by > 90% of participants to be screams, but these were not acoustically homogeneous, and others evoked mixed responses. These results suggest that while screams might represent a salient and possibly innate call type, they also exhibit perceptual and acoustic gradation, perhaps reflecting the wide range of emotions and contexts in which they occur.
KeywordsScream Non-linguistic vocalization Acoustics Roughness Forced-choice task
We thank Caitlin Clark, Alexander Gouzoules, Leah Friedman, Elizabeth Harlan, and NooRee Lee for assistance with stimulus collection, and Anna Duncan for assistance with stimulus and data collection. We also thank Anna M. Hardin and three anonymous reviewers for their comments on earlier drafts of this manuscript.
JWS was supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE – 1343012. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
- Baron-Cohen, S., & Wheelwright, S. (2004). The empathy quotient: An investigation of adults with Asperger syndrome or high functioning autism, and normal sex differences. Journal of Autism and Developmental Disorders,34(2), 163–175. https://doi.org/10.1023/B:JADD.0000022607.19833.00.CrossRefPubMedGoogle Scholar
- Bioacoustics Research Program. (2014). Raven Pro: Interactive sound analysis software (version 1.5) [Computer software]. Ithaca, NY: The Cornell Lab of Ornithology. Retrieved March 27, 2019 from http://www.birds.cornell.edu/raven.
- Boersma, P., & Weenink, D. (2013). Praat: Doing phonetics by computer (Version 5.3.51). Retrieved January 9, 2018 from http://www.praat.org/.
- Driver, P. M., & Humphries, D. A. (1969). The significance of the high-intensity alarm call in captured passerines. Ibis,111(2), 243–244. https://doi.org/10.1111/j.1474-919X.1969.tb02531.x.CrossRefGoogle Scholar
- Filippi, P., Congdon, J. V., Hoang, J., Bowling, D. L., Reber, S. A., Pašukonis, A., et al. (2017). Humans recognize emotional arousal in vocalizations across all classes of terrestrial vertebrates: Evidence for acoustic universals. Proceedings of the Royal Society B: Biological Sciences,284(1859), 20170990. https://doi.org/10.1098/rspb.2017.0990.CrossRefPubMedGoogle Scholar
- Gerosa, L., Valenzise, G., Tagliasacchi, M., Antonacci, F., & Sarti, A. (2007). Scream and gunshot detection in noisy environments. In 15th European Signal Processing Conference (pp. 1216–1220).Google Scholar
- Gifford, G. W., MacLean, K. A., Hauser, M. D., & Cohen, Y. E. (2005). The neurophysiology of functionally meaningful categories: Macaque ventrolateral prefrontal cortex plays a critical role in spontaneous categorization of species-specific vocalizations. Journal of Cognitive Neuroscience,17(9), 1471–1482. https://doi.org/10.1162/0898929054985464.CrossRefPubMedGoogle Scholar
- Golan, O., Baron-Cohen, S., Hill, J. J., & Rutherford, M. D. (2007). The “Reading the Mind in the Voice” test-revised: A study of complex emotion recognition in adults with and without autism spectrum conditions. Journal of Autism and Developmental Disorders,37(6), 1096–1106. https://doi.org/10.1007/s10803-006-0252-5.CrossRefPubMedGoogle Scholar
- Hammerschmidt, K., & Fischer, J. (1998). The vocal repertoire of Barbary macaques: A quantitative analysis of a graded signal system. Ethology,104, 203–216. https://doi.org/10.1111/j.1439-0310.1998.tb00063.x.CrossRefGoogle Scholar
- Kret, M. E., & De Gelder, B. (2012). A review on sex differences in processing emotional signals. Neuropsychologia,50(7), 1211–1221. https://doi.org/10.1016/j.neuropsychologia.2011.12.022.CrossRefGoogle Scholar
- Laffitte, P., Sodoyer, D., Tatkeu, C., & Girin, L. (2016). Deep neural networks for automatic detection of screams and shouted speech in subway trains. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 6460–6464). https://doi.org/10.1109/icassp.2016.7472921.
- Nandwana, M. K., Ziaei, A., & Hansen, J. H. L. (2015). Robust unsupervised detection of human screams in noisy acoustic environments. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 161–165). IEEE. https://doi.org/10.1109/icassp.2015.7177952.
- Owren, M. J., & Bachorowski, J. A. (2007). Measuring emotion-related vocal acoustics. In J. Coan & J. Allen (Eds.), The handbook of emotion elicitation and assessment (pp. 239–266). Oxford: Oxford University Press.Google Scholar
- R Core Team. (2018). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Retrieved October 15, 2018 from http://www.r-project.org/.
- Rand, A. S., & Ryan, M. J. (1981). The adaptive significance of a complex vocal repertoire in a neotropical frog. Zeitschrift für Tierpsychologie,57(3–4), 209–214. https://doi.org/10.1111/j.1439-0310.1981.tb01923.x.CrossRefGoogle Scholar
- Tallet, C., Linhart, P., Policht, R., Hammerschmidt, K., Šimeček, P., Kratinova, P., et al. (2013). Encoding of situations in the vocal repertoire of piglets (Sus scrofa): A comparison of discrete and graded classifications. PLoS ONE. https://doi.org/10.1371/journal.pone.0071841.CrossRefPubMedPubMedCentralGoogle Scholar