The Impact of Visual and Auditory Cues in Age Estimation

Amilon, Kajsa; Weijer, Joost van de; Schötz, Susanne

doi:10.1007/978-3-540-74122-0_2

Cited by 8 publications

(11 citation statements)

References 9 publications

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“…Globally, although age estimation is fairly good in the sense that the average percentage error is between 20 and 36 % across age groups and conditions (age of voice), it is far from perfect since these values correspond to absolute errors of 14.4 and 15.2 years for older voices estimated by older or young adults, respectively, 5.5 years for young voices estimated by young adults and 9.1 years for young voices estimated by older participants, with an overall average error of 10.8 years. This overall average error is quite similar to the 9.7 years value reported by Amilon et al (2007). These values are clearly higher than errors observed in studies of age estimation from faces using the same experimental designs, i.e.…”

Section: Discussionsupporting

confidence: 86%

“…Generally results were consistent and indicated that listeners were fairly accurate at storing a voice into an age category (Cerrato et al 2000;Ptacek and Sander 1966). When the participants' task was to assign a precise age to each voice, an age underestimation was globally observed (Amilon et al 2007;Hartman 1979) but studies using voices from an age continuum showed that the age of young adults' voices was overestimated, whereas the age of older adults' voices was strongly underestimated (Cerrato et al 2000;Harnsberger et al 2010;Hughes and Rhodes 2010;Schötz 2005;Shipp and Hollien 1969).…”

Section: Introductionmentioning

confidence: 75%

“…One way to deal with this problem is to average the absolute difference between each stimulus' perceived age and chronological age (Amilon et al 2007;Braun and Cerrato 1999;Dehon and Brédart 2001; see also Rhodes 2009). Nevertheless, the average signed values are usually calculated too, because they are informative with respect to the direction of the error; i.e., whether errors are mainly over or underestimations (Amilon et al 2007;Dehon and Brédart 2001;Rhodes 2009;Voelkle et al 2012). Unfortunately, none of the measures discussed above is very effective for comparing error magnitude for voices of different ages.…”

Section: Introductionmentioning

confidence: 99%

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Evidence for an own-age bias in age estimation from voices in older persons

2014

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Previous studies have investigated the effect of ageing on age estimation from faces as well as the occurrence of an own-age bias in such age estimation from faces. To the best of our knowledge, the occurrence of an own age effect on age estimation from voices has never been examined earlier using an experimental design in which the age of participants (young vs. old) and the age of voice stimuli (young vs. old) were crossed. Results revealed an own-age bias in older adults only. In comparison with younger adults, older participants showed age estimation abilities that are preserved for voices from their own age group and impaired for younger voices. This own age bias was absent in younger participants.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

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Evidence for an own-age bias in age estimation from voices in older persons

2014

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“…When estimating the age of ‘live’ strangers there may be additional cues to their age, such as their clothing choices (Rexbye & Povlsen, 2007) and the sound of their voice (Moyse, 2014). However, Amilon et al (2007) asked participants to estimate the age of strangers in a video where face and voice information were both available and the MAE was comparable to the no‐disguise conditions in the current experiments. Similarly, Thorley et al (2018) found sexual assault victims were able to estimate the age of stranger offenders during police interviews with an MAE comparable to the no‐disguise conditions in the current experiments.…”

Section: Discussionmentioning

confidence: 62%

How old was he? Disguises, age, and race impact upon age estimation accuracy

Thorley

2020

Applied Cognitive Psychology

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Summary There are many situations in which a person must accurately estimate a stranger's age. For example, a salesperson must accurately estimate the age of a stranger who wishes to purchase age‐restricted goods. Information from a stranger's eyes and hair can indicate their likely age. Here, two experiments examined whether adult participants' accuracy when estimating strangers' ages is reduced when the strangers' eyes are disguised by sunglasses and/or hair is disguised by a hat. The strangers' age and race also varied, and participants' social contact with the other age/race groups measured. At best, estimations of undisguised strangers' ages were inaccurate by an average of 5.10 years. Accuracy decreased when the strangers' eyes, but not hair, were disguised and when they came from other age/race groups. Accuracy when estimating other age/race group members' ages and social contact with their members were unrelated. The theoretical and applied implications of these findings are discussed.

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“…A recent review of previous studies on human listener judgments of speaker age may be found in [3]. Of the studies reported which used numerical age estimation and [5] 7.1 Limited age-range Amilon et al, 2009 [6] 9.7 Moyse et al, 2014 [7] 10.8…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Human and Machine Estimation of Speaker Age

Huckvale

Webb

2015

Statistical Language and Speech Processing

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Abstract. The estimation of the age of a speaker from his or her voice has both forensic and commercial applications. Previous studies have shown that human listeners are able to estimate the age of a speaker to within 10 years on average, while recent machine age estimation systems seem to show superior performance with average errors as low as 6 years. However the machine studies have used highly non-uniform test sets, for which knowledge of the age distribution offers considerable advantage to the system. In this study we compare human and machine performance on the same test data chosen to be uniformly distributed in age. We show that in this case human and machine accuracy is more similar with average errors of 9.8 and 8.6 years respectively, although if panels of listeners are consulted, human accuracy can be improved to a value closer to 7.5 years. Both human and machines have difficulty in accurately predicting the ages of older speakers.Keywords: speaker profiling, speaker age prediction, computational paralinguistics. IntroductionThe estimation of the age of a speaker from an analysis of his or her voice has forensic applications -for example the profiling of perpetrators of crimes [1], commercial applications -for example targeted advertising, and technological applicationsfor example adaptation of a speech recognition system to a speaker [2].Many previous studies have looked at the performance of both human listeners and machine-learning systems for the estimation of age from speech. Unfortunately, variations in the data set, task and performance metric make these studies hard to compare. In our work we take the view that the natural task should be numerical estimation of the age of the speaker, and the natural performance metric should be the mean absolute error (MAE) of estimation. The MAE answers the question "how close is the average estimate to the actual age?" A recent review of previous studies on human listener judgments of speaker age may be found in [3]. Of the studies reported which used numerical age estimation and

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The Impact of Visual and Auditory Cues in Age Estimation

Cited by 8 publications

References 9 publications

Evidence for an own-age bias in age estimation from voices in older persons

Evidence for an own-age bias in age estimation from voices in older persons

How old was he? Disguises, age, and race impact upon age estimation accuracy

A Comparison of Human and Machine Estimation of Speaker Age

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