The role of loudness in vocal intimidation.

Across many species, a major function of vocal communication is to convey formidability, with low voice frequencies traditionally considered the main vehicle for projecting large size and aggression. Vocal loudness is often ignored, yet it might explain some puzzling exceptions to this frequency code. Here we demonstrate, through acoustic analyses of over 3,000 human vocalizations and four perceptual experiments, that vocalizers produce low frequencies when attempting to sound large, but loudness is prioritized for displays of strength and aggression. Our results show that, although being loud is effective for signaling strength and aggression, it poses a physiological trade-off with low frequencies because a loud voice is achieved by elevating pitch and opening the mouth wide into a-like vowels. This may explain why aggressive vocalizations are often high-pitched and why open vowels are considered "large" in sound symbolism despite their high first formant. Callers often compensate by adding vocal harshness (nonlinear vocal phenomena) to undesirably high-pitched loud vocalizations, but a combination of low and loud remains an honest predictor of both perceived and actual physical formidability. The proposed notion of a loudness-frequency trade-off thus adds a new dimension to the widely accepted frequency code and requires a fundamental rethinking of the evolutionary forces shaping the form of acoustic signals. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Vocal communication: The enigmatic production of low-frequency purrs in cats.

Cat purring, the unusual, pulsed vibration that epitomizes comfort, enjoys a special status in the world of vocal communication research. Indeed, it has long been flagged as a rare exception to the dominant theory of voice production in mammals. A new study presents histological and biomechanical evidence that purring can occur passively, without needing muscle vibration in the larynx controlled by an independent neural oscillator.

A practical guide to calculating vocal tract length and scale-invariant formant patterns.

Formants (vocal tract resonances) are increasingly analyzed not only by phoneticians in speech but also by behavioral scientists studying diverse phenomena such as acoustic size exaggeration and articulatory abilities of non-human animals. This often involves estimating vocal tract length acoustically and producing scale-invariant representations of formant patterns. We present a theoretical framework and practical tools for carrying out this work, including open-source software solutions included in R packages soundgen and phonTools. Automatic formant measurement with linear predictive coding is error-prone, but formant_app provides an integrated environment for formant annotation and correction with visual and auditory feedback. Once measured, formants can be normalized using a single recording (intrinsic methods) or multiple recordings from the same individual (extrinsic methods). Intrinsic speaker normalization can be as simple as taking formant ratios and calculating the geometric mean as a measure of overall scale. The regression method implemented in the function estimateVTL calculates the apparent vocal tract length assuming a single-tube model, while its residuals provide a scale-invariant vowel space based on how far each formant deviates from equal spacing (the schwa function). Extrinsic speaker normalization provides more accurate estimates of speaker- and vowel-specific scale factors by pooling information across recordings with simple averaging or mixed models, which we illustrate with example datasets and R code. The take-home messages are to record several calls or vowels per individual, measure at least three or four formants, check formant measurements manually, treat uncertain values as missing, and use the statistical tools best suited to each modeling context.

Beyond speech: Exploring diversity in the human voice.

Humans have evolved voluntary control over vocal production for speaking and singing, while preserving the phylogenetically older system of spontaneous nonverbal vocalizations such as laughs and screams. To test for systematic acoustic differences between these vocal domains, we analyzed a broad, cross-cultural corpus representing over 2 h of speech, singing, and nonverbal vocalizations. We show that, while speech is relatively low-pitched and tonal with mostly regular phonation, singing and especially nonverbal vocalizations vary enormously in pitch and often display harsh-sounding, irregular phonation owing to nonlinear phenomena. The evolution of complex supralaryngeal articulatory spectro-temporal modulation has been critical for speech, yet has not significantly constrained laryngeal source modulation. In contrast, articulation is very limited in nonverbal vocalizations, which predominantly contain minimally articulated open vowels and rapid temporal modulation in the roughness range. We infer that vocal source modulation works best for conveying affect, while vocal filter modulation mainly facilitates semantic communication.

The honest sound of physical effort.

Acoustic correlates of physical effort are still poorly understood, even though effort is vocally communicated in a variety of contexts with crucial fitness consequences, including both confrontational and reproductive social interactions. In this study 33 lay participants spoke during a brief, but intense isometric hold (L-sit), first without any voice-related instructions, and then asked either to conceal their effort or to imitate it without actually performing the exercise. Listeners in two perceptual experiments then rated 383 recordings on perceived level of effort (n = 39 listeners) or categorized them as relaxed speech, actual effort, pretended effort, or concealed effort (n = 102 listeners). As expected, vocal effort increased compared to baseline, but the accompanying acoustic changes (increased loudness, pitch, and tense voice quality) were under voluntary control, so that they could be largely suppressed or imitated at will. In contrast, vocal tremor at approximately 10 Hz was most pronounced under actual load, and its experimental addition to relaxed baseline recordings created the impression of concealed effort. In sum, a brief episode of intense physical effort causes pronounced vocal changes, some of which are difficult to control. Listeners can thus estimate the true level of exertion, whether to judge the condition of their opponent in a fight or to monitor a partner's investment into cooperative physical activities.

Do some languages sound more beautiful than others?

Italian is sexy, German is rough-but how about Páez or Tamil? Are there universal phonesthetic judgments based purely on the sound of a language, or are preferences attributable to language-external factors such as familiarity and cultural stereotypes? We collected 2,125 recordings of 228 languages from 43 language families, including 5 to 11 speakers of each language to control for personal vocal attractiveness, and asked 820 native speakers of English, Chinese, or Semitic languages to indicate how much they liked these languages. We found a strong preference for languages perceived as familiar, even when they were misidentified, a variety of cultural-geographical biases, and a preference for breathy female voices. The scores by English, Chinese, and Semitic speakers were weakly correlated, indicating some cross-cultural concordance in phonesthetic judgments, but overall there was little consensus between raters about which languages sounded more beautiful, and average scores per language remained within ±2% after accounting for confounds related to familiarity and voice quality of individual speakers. None of the tested phonetic features-the presence of specific phonemic classes, the overall size of phonetic repertoire, its typicality and similarity to the listener's first language-were robust predictors of pleasantness ratings, apart from a possible slight preference for nontonal languages. While population-level phonesthetic preferences may exist, their contribution to perceptual judgments of short speech recordings appears to be minor compared to purely personal preferences, the speaker's voice quality, and perceived resemblance to other languages culturally branded as beautiful or ugly.

Nonlinear vocal phenomena affect human perceptions of distress, size and dominance in puppy whines.

While nonlinear phenomena (NLP) are widely reported in animal vocalizations, often causing perceptual harshness and roughness, their communicative function remains debated. Several hypotheses have been put forward: attention-grabbing, communication of distress, exaggeration of body size and dominance. Here, we use state-of-the-art sound synthesis to investigate how NLP affect the perception of puppy whines by human listeners. Listeners assessed the distress, size or dominance conveyed by synthetic puppy whines with manipulated NLP, including frequency jumps and varying proportions of subharmonics, sidebands and deterministic chaos. We found that the presence of chaos increased the puppy's perceived level of distress and that this effect held across a range of representative fundamental frequency (fo) levels. Adding sidebands and subharmonics also increased perceived distress among listeners who have extensive caregiving experience with pre-weaned puppies (e.g. breeders, veterinarians). Finally, we found that whines with added chaos, subharmonics or sidebands were associated with larger and more dominant puppies, although these biases were attenuated in experienced caregivers. Together, our results show that nonlinear phenomena in puppy whines can convey rich information to human listeners and therefore may be crucial for offspring survival during breeding of a domesticated species.

Static and dynamic formant scaling conveys body size and aggression.

When producing intimidating aggressive vocalizations, humans and other animals often extend their vocal tracts to lower their voice resonance frequencies (formants) and thus sound big. Is acoustic size exaggeration more effective when the vocal tract is extended before, or during, the vocalization, and how do listeners interpret within-call changes in apparent vocal tract length? We compared perceptual effects of static and dynamic formant scaling in aggressive human speech and nonverbal vocalizations. Acoustic manipulations corresponded to elongating or shortening the vocal tract either around (Experiment 1) or from (Experiment 2) its resting position. Gradual formant scaling that preserved average frequencies conveyed the impression of smaller size and greater aggression, regardless of the direction of change. Vocal tract shortening from the original length conveyed smaller size and less aggression, whereas vocal tract elongation conveyed larger size and more aggression, and these effects were stronger for static than for dynamic scaling. Listeners familiarized with the speaker's natural voice were less often 'fooled' by formant manipulations when judging speaker size, but paid more attention to formants when judging aggressive intent. Thus, within-call vocal tract scaling conveys emotion, but a better way to sound large and intimidating is to keep the vocal tract consistently extended.

Vocal size exaggeration may have contributed to the origins of vocalic complexity.

Vocal tract elongation, which uniformly lowers vocal tract resonances (formant frequencies) in animal vocalizations, has evolved independently in several vertebrate groups as a means for vocalizers to exaggerate their apparent body size. Here, we propose that smaller speech-like articulatory movements that alter only individual formants can serve a similar yet less energetically costly size-exaggerating function. To test this, we examine whether uneven formant spacing alters the perceived body size of vocalizers in synthesized human vowels and animal calls. Among six synthetic vowel patterns, those characterized by the lowest first and second formant (the vowel /u/ as in 'boot') are consistently perceived as produced by the largest vocalizer. Crucially, lowering only one or two formants in animal-like calls also conveys the impression of a larger body size, and lowering the second and third formants simultaneously exaggerates perceived size to a similar extent as rescaling all formants. As the articulatory movements required for individual formant shifts are minor compared to full vocal tract extension, they represent a rapid and energetically efficient mechanism for acoustic size exaggeration. We suggest that, by favouring the evolution of uneven formant patterns in vocal communication, this deceptive strategy may have contributed to the origins of the phonemic diversification required for articulated speech. This article is part of the theme issue 'Voice modulation: from origin and mechanism to social impact (Part II)'.

Emotional authenticity modulates affective and social trait inferences from voices.

The human voice is a primary tool for verbal and nonverbal communication. Studies on laughter emphasize a distinction between spontaneous laughter, which reflects a genuinely felt emotion, and volitional laughter, associated with more intentional communicative acts. Listeners can reliably differentiate the two. It remains unclear, however, if they can detect authenticity in other vocalizations, and whether authenticity determines the affective and social impressions that we form about others. Here, 137 participants listened to laughs and cries that could be spontaneous or volitional and rated them on authenticity, valence, arousal, trustworthiness and dominance. Bayesian mixed models indicated that listeners detect authenticity similarly well in laughter and crying. Speakers were also perceived to be more trustworthy, and in a higher arousal state, when their laughs and cries were spontaneous. Moreover, spontaneous laughs were evaluated as more positive than volitional ones, and we found that the same acoustic features predicted perceived authenticity and trustworthiness in laughter: high pitch, spectral variability and less voicing. For crying, associations between acoustic features and ratings were less reliable. These findings indicate that emotional authenticity shapes affective and social trait inferences from voices, and that the ability to detect authenticity in vocalizations is not limited to laughter. This article is part of the theme issue 'Voice modulation: from origin and mechanism to social impact (Part I)'.

Harsh is large: nonlinear vocal phenomena lower voice pitch and exaggerate body size.

A lion's roar, a dog's bark, an angry yell in a pub brawl: what do these vocalizations have in common? They all sound harsh due to nonlinear vocal phenomena (NLP)-deviations from regular voice production, hypothesized to lower perceived voice pitch and thereby exaggerate the apparent body size of the vocalizer. To test this yet uncorroborated hypothesis, we synthesized human nonverbal vocalizations, such as roars, groans and screams, with and without NLP (amplitude modulation, subharmonics and chaos). We then measured their effects on nearly 700 listeners' perceptions of three psychoacoustic (pitch, timbre, roughness) and three ecological (body size, formidability, aggression) characteristics. In an explicit rating task, all NLP lowered perceived voice pitch, increased voice darkness and roughness, and caused vocalizers to sound larger, more formidable and more aggressive. Key results were replicated in an implicit associations test, suggesting that the 'harsh is large' bias will arise in ecologically relevant confrontational contexts that involve a rapid, and largely implicit, evaluation of the opponent's size. In sum, nonlinearities in human vocalizations can flexibly communicate both formidability and intention to attack, suggesting they are not a mere byproduct of loud vocalizing, but rather an informative acoustic signal well suited for intimidating potential opponents.

Authentic and posed emotional vocalizations trigger distinct facial responses.

The ability to recognize the emotions of others is a crucial skill. In the visual modality, sensorimotor mechanisms provide an important route for emotion recognition. Perceiving facial expressions often evokes activity in facial muscles and in motor and somatosensory systems, and this activity relates to performance in emotion tasks. It remains unclear whether and how similar mechanisms extend to audition. Here we examined facial electromyographic and electrodermal responses to nonverbal vocalizations that varied in emotional authenticity. Participants (N = 100) passively listened to laughs and cries that could reflect an authentic or a posed emotion. Bayesian mixed models indicated that listening to laughter evoked stronger facial responses than listening to crying. These responses were sensitive to emotional authenticity. Authentic laughs evoked more activity than posed laughs in the zygomaticus and orbicularis, muscles typically associated with positive affect. We also found that activity in the orbicularis and corrugator related to subjective evaluations in a subsequent authenticity perception task. Stronger responses in the orbicularis predicted higher perceived laughter authenticity. Stronger responses in the corrugator, a muscle associated with negative affect, predicted lower perceived laughter authenticity. Moreover, authentic laughs elicited stronger skin conductance responses than posed laughs. This arousal effect did not predict task performance, however. For crying, physiological responses were not associated with authenticity judgments. Altogether, these findings indicate that emotional authenticity affects peripheral nervous system responses to vocalizations. They also point to a role of sensorimotor mechanisms in the evaluation of authenticity in the auditory modality.

Changes in vocal emotion recognition across the life span.

The ability to recognize emotions undergoes major developmental changes from infancy to adolescence, peaking in early adulthood, and declining with aging. A life span approach to emotion recognition is lacking in the auditory domain, and it remains unclear how the speaker's and listener's ages interact in the context of decoding vocal emotions. Here, we examined age-related differences in vocal emotion recognition from childhood until older adulthood and tested for a potential own-age bias in performance. A total of 164 participants (36 children [7-11 years], 53 adolescents [12-17 years], 48 young adults [20-30 years], 27 older adults [58-82 years]) completed a forced-choice emotion categorization task with nonverbal vocalizations expressing pleasure, relief, achievement, happiness, sadness, disgust, anger, fear, surprise, and neutrality. These vocalizations were produced by 16 speakers, 4 from each age group (children [8-11 years], adolescents [14-16 years], young adults [19-23 years], older adults [60-75 years]). Accuracy in vocal emotion recognition improved from childhood to early adulthood and declined in older adults. Moreover, patterns of improvement and decline differed by emotion category: faster development for pleasure, relief, sadness, and surprise and delayed decline for fear and surprise. Vocal emotions produced by older adults were more difficult to recognize when compared to all other age groups. No evidence for an own-age bias was found, except in children. These findings support effects of both speaker and listener ages on how vocal emotions are decoded and inform current models of vocal emotion perception. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The link between auditory salience and emotion intensity.

To ensure that listeners pay attention and do not habituate, emotionally intense vocalizations may be under evolutionary pressure to exploit processing biases in the auditory system by maximising their bottom-up salience. This "salience code" hypothesis was tested using 128 human nonverbal vocalizations representing eight emotions: amusement, anger, disgust, effort, fear, pain, pleasure, and sadness. As expected, within each emotion category salience ratings derived from pairwise comparisons strongly correlated with perceived emotion intensity. For example, while laughs as a class were less salient than screams of fear, salience scores almost perfectly explained the perceived intensity of both amusement and fear considered separately. Validating self-rated salience evaluations, high- vs. low-salience sounds caused 25% more recall errors in a short-term memory task, whereas emotion intensity had no independent effect on recall errors. Furthermore, the acoustic characteristics of salient vocalizations were similar to those previously described for non-emotional sounds (greater duration and intensity, high pitch, bright timbre, rapid modulations, and variable spectral characteristics), confirming that vocalizations were not salient merely because of their emotional content. The acoustic code in nonverbal communication is thus aligned with sensory biases, offering a general explanation for some non-arbitrary properties of human and animal high-arousal vocalizations.

A Moan of Pleasure Should Be Breathy: The Effect of Voice Quality on the Meaning of Human Nonverbal Vocalizations.

Prosodic features, such as intonation and voice intensity, have a well-documented role in communicating emotion, but less is known about the role of laryngeal voice quality in speech and particularly in nonverbal vocalizations such as laughs and moans. Potentially, however, variations in voice quality between tense and breathy may convey rich information about the speaker's physiological and affective state. In this study breathiness was manipulated in synthetic human nonverbal vocalizations by adjusting the relative strength of upper harmonics and aspiration noise. In experiment 1 (28 prototypes × 3 manipulations = 84 sounds), otherwise identical vocalizations with tense versus breathy voice quality were associated with higher arousal (general alertness), higher dominance, and lower valence (unpleasant states). Ratings on discrete emotions in experiment 2 (56 × 3 = 168 sounds) confirmed that breathiness was reliably associated with positive emotions, particularly in ambiguous vocalizations (gasps and moans). The spectral centroid did not fully account for the effect of manipulation, confirming that the perceived change in voice quality was more specific than a general shift in timbral brightness. Breathiness is thus involved in communicating emotion with nonverbal vocalizations, possibly due to changes in low-level auditory salience and perceived vocal effort.

Do nonlinear vocal phenomena signal negative valence or high emotion intensity?

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.

Spatial location and emotion modulate voice perception.

How do we perceive voices coming from different spatial locations, and how is this affected by emotion? The current study probed the interplay between space and emotion during voice perception. Thirty participants listened to nonverbal vocalizations coming from different locations around the head (left vs. right; front vs. back), and differing in valence (neutral, positive [amusement] or negative [anger]). They were instructed to identify the location of the vocalizations (Experiment 1) and to evaluate their emotional qualities (Experiment 2). Emotion-space interactions were observed, but only in Experiment 1: emotional vocalizations were better localised than neutral ones when they were presented from the back and the right side. In Experiment 2, emotion recognition accuracy was increased for positive vs. negative and neutral vocalizations, and perceived arousal was increased for emotional vs. neutral vocalizations, but this was independent of spatial location. These findings indicate that emotional salience affects how we perceive the spatial location of voices. They additionally suggest that the interaction between spatial ("where") and emotional ("what") properties of the voice differs as a function of task.

Implicit associations between individual properties of color and sound.

We report a series of 22 experiments in which the implicit associations test (IAT) was used to investigate cross-modal correspondences between visual (luminance, hue [R-G, B-Y], saturation) and acoustic (loudness, pitch, formants [F1, F2], spectral centroid, trill) dimensions. Colors were sampled from the perceptually accurate CIE-Lab space, and the complex, vowel-like sounds were created with a formant synthesizer capable of separately manipulating individual acoustic properties. In line with previous reports, the loudness and pitch of acoustic stimuli were associated with both luminance and saturation of the presented colors. However, pitch was associated specifically with color lightness, whereas loudness mapped onto greater visual saliency. Manipulating the spectrum of sounds without modifying their pitch showed that an upward shift of spectral energy was associated with the same visual features (higher luminance and saturation) as higher pitch. In contrast, changing formant frequencies of synthetic vowels while minimizing the accompanying shifts in spectral centroid failed to reveal cross-modal correspondences with color. This may indicate that the commonly reported associations between vowels and colors are mediated by differences in the overall balance of low- and high-frequency energy in the spectrum rather than by vowel identity as such. Surprisingly, the hue of colors with the same luminance and saturation was not associated with any of the tested acoustic features, except for a weak preference to match higher pitch with blue (vs. yellow). We discuss these findings in the context of previous research and consider their implications for sound symbolism in world languages.

Soundgen: An open-source tool for synthesizing nonverbal vocalizations.

Voice synthesis is a useful method for investigating the communicative role of different acoustic features. Although many text-to-speech systems are available, researchers of human nonverbal vocalizations and bioacousticians may profit from a dedicated simple tool for synthesizing and manipulating natural-sounding vocalizations. Soundgen ( https://CRAN.R-project.org/package=soundgen ) is an open-source R package that synthesizes nonverbal vocalizations based on meaningful acoustic parameters, which can be specified from the command line or in an interactive app. This tool was validated by comparing the perceived emotion, valence, arousal, and authenticity of 60 recorded human nonverbal vocalizations (screams, moans, laughs, and so on) and their approximate synthetic reproductions. Each synthetic sound was created by manually specifying only a small number of high-level control parameters, such as syllable length and a few anchors for the intonation contour. Nevertheless, the valence and arousal ratings of synthetic sounds were similar to those of the original recordings, and the authenticity ratings were comparable, maintaining parity with the originals for less complex vocalizations. Manipulating the precise acoustic characteristics of synthetic sounds may shed light on the salient predictors of emotion in the human voice. More generally, soundgen may prove useful for any studies that require precise control over the acoustic features of nonspeech sounds, including research on animal vocalizations and auditory perception.

Automaticity in the recognition of nonverbal emotional vocalizations.

The ability to perceive the emotions of others is crucial for everyday social interactions. Important aspects of visual socioemotional processing, such as the recognition of facial expressions, are known to depend on largely automatic mechanisms. However, whether and how properties of automaticity extend to the auditory domain remains poorly understood. Here we ask if nonverbal auditory emotion recognition is a controlled deliberate or an automatic efficient process, using vocalizations such as laughter, crying, and screams. In a between-subjects design (N = 112), and covering eight emotions (four positive), we determined whether emotion recognition accuracy (a) is improved when participants actively deliberate about their responses (compared with when they respond as fast as possible) and (b) is impaired when they respond under low and high levels of cognitive load (concurrent task involving memorizing sequences of six or eight digits, respectively). Response latencies were also measured. Mixed-effects models revealed that recognition accuracy was high across emotions, and only minimally affected by deliberation and cognitive load; the benefits of deliberation and costs of cognitive load were significant mostly for positive emotions, notably amusement/laughter, and smaller or absent for negative ones; response latencies did not suffer under low or high cognitive load; and high recognition accuracy (approximately 90%) could be reached within 500 ms after the stimulus onset, with performance exceeding chance-level already between 300 and 360 ms. These findings indicate that key features of automaticity, namely fast and efficient/effortless processing, might be a modality-independent component of emotion recognition. (PsycINFO Database Record (c) 2019 APA, all rights reserved).