Abstract

Nonlinear vocal phenomena (NLPs) are commonly reported in animal calls and, increasingly, in human vocalizations. These perceptually harsh and chaotic voice features function to attract attention and convey urgency, but they may also signal aversive states. To test whether NLPs enhance the perception of negative affect or only signal high arousal, we added subharmonics, sidebands or deterministic chaos to 48 synthetic human nonverbal vocalizations of ambiguous valence: gasps of fright/surprise, moans of pain/pleasure, roars of frustration/achievement and screams of fear/delight. In playback experiments (N = 900 listeners), we compared their perceived valence and emotion intensity in positive or negative contexts or in the absence of any contextual cues. Primarily, NLPs increased the perceived aversiveness of vocalizations regardless of context. To a smaller extent, they also increased the perceived emotion intensity, particularly when the context was negative or absent. However, NLPs also enhanced the perceived intensity of roars of achievement, indicating that their effects can generalize to positive emotions. In sum, a harsh voice with NLPs strongly tips the balance towards negative emotions when a vocalization is ambiguous, but with sufficiently informative contextual cues, NLPs may be re-evaluated as expressions of intense positive affect, underlining the importance of context in nonverbal communication.

Highlights

  • In this study, we explore an understudied yet highly biologically and socially relevant set of voice features—nonlinear vocal phenomena (NLPs), which contribute to a harsh or rough voice frequency royalsocietypublishing.org/journal/rsos R

  • The effect of context on the perceived emotion intensity was too small and uncertain to detect for gasps and for roars

  • We investigated the communicative significance of a harsh or rough voice quality caused by nonlinear phenomena (NLPs) in human nonverbal vocalizations, which were synthesized in three versions: without NLPs, with controlled amounts of subharmonics and sidebands, or with deterministic chaos

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Summary

Introduction

We explore an understudied yet highly biologically and socially relevant set of voice features—nonlinear vocal phenomena (NLPs), which contribute to a harsh or rough voice frequency (kHz) royalsocietypublishing.org/journal/rsos R. NLPs result from perturbations in the typical rhythmic vibration of the vocal folds that cause deviations from regular, tonal voice production (see [1,2,3,4] for reviews). This can result in frequency jumps, subharmonics, sidebands and deterministic chaos (figure 1), which give the voice a perceptual quality of harshness, roughness or instability. NLPs render a signal less predictable, which attracts attention [7] and reduces habituation in listeners [8] They are often treated as an expression of high arousal or emotion intensity [6,7,11]. Recent research has shown that rapid spectrotemporal modulation correlates with the aversiveness of artificial click trains [19] and with the perceived fear intensity in human screams [12], suggesting that listeners may experience both amplitude modulation (sidebands) and chaos as unpleasant

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