A sound effect: <scp>E</scp>xploration of the distinctiveness advantage in voice recognition

Stevenage, Sarah V.; Neil, Greg J.; Parsons, Beth; Humphreys, Abi

doi:10.1002/acp.3424

Cited by 7 publications

(6 citation statements)

References 43 publications

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“…Remaining differences between voices should be idiosyncratic, so that the features that differentiate pairs of talkers depend on the precise acoustic information involved in each comparison (e.g., Kreiman and Gerratt 1996). This would be consistent with what has been found for faces (Maguinness et al, 2018;Stevenage et al, 2018;Yovel and Belin, 2013), although we cannot assume that faces and voices are perceived in similar ways at all processing stages.…”

Section: Existing Cognitive and Neuropsychological Models Of Voice Pesupporting

confidence: 62%

“…Facial recognition poses similar challenges to viewers, who must cope with changes in lighting, expression, and orientation in order to identify or discriminate among faces (Hill and Bruce, 1996;O'Toole et al, 1998;Patterson and Baddeley, 1977). Because similarities exist in voice and face processing (Stevenage et al, 2018;Yovel and Belin, 2013), recent findings from the face perception literature may provide insight into mechanisms for coping with acoustic voice variability. In particular, facial identity learning improves when viewers are exposed to highly but naturally varying sets of images of one person (for example, with changes in orientation or emotion) during training (Kramer et al, 2017;Murphy et al, 2015; Ritchie and Burton, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Acoustic voice variation within and between speakers

Lee

Keating

Kreiman

2019

The Journal of the Acoustical Society of America

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Little is known about the nature or extent of everyday variability in voice quality. This paper describes a series of principal component analyses to explore within-and between-talker acoustic variation and the extent to which they conform to expectations derived from current models of voice perception. Based on studies of faces and cognitive models of speaker recognition, we hypothesized that a few measures would be important across speakers, but that much of within-speaker variability would be idiosyncratic. Analyses used multiple sentence productions from fifty female and fifty male speakers of English, recorded over three days. Twenty-six acoustic variables from a psychoacoustic model of voice quality were measured every 5 ms on vowels and approximants. Across speakers the balance between higher harmonic amplitudes and inharmonic energy in the voice accounted for the most variance (fe-males=20%, males=22%). Formant frequencies and their variability accounted for an additional 12% of variance across speakers. Remaining variance appeared largely idiosyncratic, suggesting that the speaker-specific voice space is different for different people. Results further showed that voice spaces for individuals and for the population of talkers have very similar acoustic structures. Implications for prototype models of voice perception and recognition are discussed.

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Section: Existing Cognitive and Neuropsychological Models Of Voice Pesupporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Acoustic voice variation within and between speakers

Lee

Keating

Kreiman

2019

The Journal of the Acoustical Society of America

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“…At the same time, ID3 does not stand out as being relatively easier to ‘tell together’ for the audio‐only stimuli. It is thus likely that the face of ID3 featured a diagnostic or distinctive feature that participants were able to pick up on and use to ‘tell together’ the different, variable stimuli more succesfully, while there was no such diagnostic or distinctive feature in the voice of ID3 (see Stevenage, Neil, Parsons, & Humphreys, 2018; Valentine, 1991). Albeit to a lesser degree, there are also modality‐differences apparent for ID1.…”

Section: Discussionmentioning

confidence: 99%

Audiovisual identity perception from naturally‐varying stimuli is driven by visual information

Lavan

Collins

Miah

2021

British J of Psychology

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Identity perception often takes place in multimodal settings, where perceivers have access to both visual (face) and auditory (voice) information. Despite this, identity perception is usually studied in unimodal contexts, where face and voice identity perception are modelled independently from one another. In this study, we asked whether and how much auditory and visual information contribute to audiovisual identity perception from naturally-varying stimuli. In a between-subjects design, participants completed an identity sorting task with either dynamic video-only, audio-only or dynamic audiovisual stimuli. In this task, participants were asked to sort multiple, naturally-varying stimuli from three different people by perceived identity. We found that identity perception was more accurate for video-only and audiovisual stimuli compared with audio-only stimuli. Interestingly, there was no difference in accuracy between video-only and audiovisual stimuli. Auditory information nonetheless played a role alongside visual information as audiovisual identity judgements per stimulus could be predicted from both auditory and visual identity judgements, respectively. While the relationship was stronger for visual information and audiovisual information, auditory information still uniquely explained a significant portion of the variance in audiovisual identity judgements. Our findings thus align with previous theoretical and empirical work that proposes that, compared with faces, voices are an important but relatively less salient and a weaker cue to identity perception. We expand on this work to show that, at least in the context of this study, having access to voices in addition to faces does not result in better identity perception accuracy.We can perceive identity-related information from both faces and voices independently (Belin, Bestelmeyer, Latinus, & Watson, 2011;Bruce & Young, 1986). However, cues to identity perception from these two sources of information are often seen as working broadly in parallel while also having the potential to be complementary to one another (Young, Fr€ uhholz, & Schweinberger, 2020;Yovel & Belin, 2013). Similarly, interactions between the visual and auditory modalities during identity perception have often beenThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…With a testing time of ~22 minutes, the JVLMT is time-efficient and can be easily integrated in research projects investigating voice processing abilities among the general population and how these abilities are linked with other aspects of perception and cognition. Prospectively, it can also be used as a screening tool in clinical settings to help detect voice recognition impairments in neurological (Neuner & Schweinberger, 2000), or psychiatric patients (Stevenage, 2018), particularly in autism (Schelinski et al, 2017), or other conditions in which voice recognition difficulties may be more prevalent without being routinely screened for (Neuner & Schweinberger, 2000).…”

Section: Applications and Limitationsmentioning

confidence: 99%

The Jena Voice Learning and Memory Test (JVLMT): A standardized tool for assessing the ability to learn and recognize voices

Humble¹,

Schweinberger²,

Mayer³

et al. 2021

Preprint

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The ability to recognize someone’s voice exists on a broad spectrum with phonagnosia on the low end and super recognition at the high end. Yet there is no standardized test to measure an individual’s ability of learning and recognizing newly-learnt voices with samples of speech-like phonetic variability. We have developed the Jena Voice Learning and Memory Test (JVLMT), a 22min-test based on item response theory and applicable across languages. The JVLMT consists of three phases in which participants first become familiarized with eight speakers and then perform a three-alternative forced choice recognition task, using pseudo sentences devoid of semantics. Acoustic (dis)similarity analyses were used to create items with different levels of difficulty. Test scores are based on 22 Rasch-conform items. Items were selected based on 232 and validated based on 454 participants in an online study. Mean accuracy is 0.51 with an SD of .18. The JVLMT showed high and moderate correlations with the convergent validation tests (Bangor Voice Matching Test; Glasgow Voice Memory Test, respectively) and a weak correlation with the discriminant validation test (Digit Span). Empirical (marginal) reliability is 0.66. Four participants with super recognition abilities and 7 participants with phonagnosia were identified (at least 2 SDs above or below the mean, respectively).The JVLMT is a promising diagnostic tool to screen for voice recognition abilities in a scientific and neuropsychological context.

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A sound effect: Exploration of the distinctiveness advantage in voice recognition

Cited by 7 publications

References 43 publications

Acoustic voice variation within and between speakers

Acoustic voice variation within and between speakers

Audiovisual identity perception from naturally‐varying stimuli is driven by visual information

The Jena Voice Learning and Memory Test (JVLMT): A standardized tool for assessing the ability to learn and recognize voices

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