Making Age Assessments Based on Voice

Journal of Social, Evolutionary, and Cultural Psychology www.jsecjournal.com - 2010, 4(4), 290-304. Proceedings of the 4th Annual Meeting of the NorthEastern Evolutionary Psychology Society

2010 Journal of Social, Evolutionary, and Cultural Psychology

290

Original Article

MAKING AGE ASSESSMENTS BASED ON VOICE: THE IMPACT OF THE REPRODUCTIVE VIABILITY OF THE SPEAKER

Susan Hughes*

Department of Psychology, Albright College

Bradley C. Rhodes Department of Biology, Bucknell University

Abstract This study examined the ability to make age estimates based upon hearing voice samples of speakers whose ages vary across the lifespan while considering the raters own age and sex. It was hypothesized that voices are a strong index of reproductive viability and therefore, members of both sexes would be most accurate in assessing age of those around puberty and females approaching menopause. Voice samples were obtained from 101 individuals of both sexes, ranging in age from 2 to 67 years and an additional 97 independent raters of different ages were asked to estimate the exact age of the speakers from voice recordings. Results showed that accuracy of voice assessment tended to decrease as the speakers ages increased, with assessments of children and adolescents being the most accurate. Overall, raters tended to underestimate the age of speakers as the speaker age increased regardless of the raters own age. Whereas accuracy in ratings decreased when male speakers reached age 46-55, accuracy remained high for female speakers in their menopausal years, suggesting that both sexes are sensitive to vocal changes during this developmental period. These findings illustrate that the human voice may be used as a salient cue for assessing reproductive viability. Keywords: Voice, age assessments, puberty, menopause, reproductive viability

Introduction

Human voices can convey a considerable amount of information to a listener and can be used to assess a number of qualities about a person. For instance, several studies have shown that listeners can determine a variety of physical attributes of speakers such as gender, race, height, weight, and other body dimensions by simply hearing their voice (see Hughes & Gallup, 2008 for review). Individuals of African and European descent can be differentiated from one another from voice samples (Lass, Almero, Jordan, & Walsh, 1980; Lass et al., 1978; Lass, Tecca, et al., 1979; Lass et al., 1982). Height can be estimated, on average, to within a half an inch of a persons actual height from hearing a AUTHOR NOTE: Please direct correspondence to Susan Hughes, Psychology Department, Albright College, 13th and Bern Streets, Reading, PA 19612. Email: [email protected]

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Susan M. Hughes

Age Assessments

Journal of Social, Evolutionary, and Cultural Psychology ISSN 1933-5377 Volume 4(4). 2010.

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persons voice only (Lass, DiCola, et al., 1979; Krauss, Freyberg & Morsella, 2002). Weight, too, can be estimated accurately by listening to a voice only (Lass et al., 1979), and with nearly as much accuracy as assessing weight from seeing a full frontal photograph (Krauss et al., 2002). People can also match a speakers photograph with their voice sample in a forced choice experiment 76% percent of the time (Krauss et al., 2002).

The sex of the speaker can also be determined with a high degree of accuracy from both unaltered or filtered voice recordings (Cerrato, Falcone and Paoloni, 1999; Lass, Almero, Jordan and Walsh, 1980; Lass, Hughes, Bowyer, Waters and Bourne, 1976), when only presented with isolated vowel sounds (Lass, Tecca, Mancuso, & Black, 1979), or unvoiced fricatives (Schwartz et al., 1983), and even when recordings are played backwards (Lass, Mertz, & Kimmel, 1978). Speakers can also effectively attempt to make their voice sound like that of a member of the other sex (Lass, Trapp, Baldwin, Scherbick, & Wright, 1982). Apparently, manipulating both the fundamental frequency and formants of the voice is enough to change the sound of a voice as if coming from the opposite sex of the source (Hillenbrand & Clark, 2009).

In addition to using voice to identify different features of a person, voice attractiveness can also be used to make assessments about a person. Voice attractiveness is positively correlated with a number of objective measures of body attractiveness such as waist-to-hip ratio in women, shoulder-to-hip ratio in men (Hughes, Dispenza & Gallup, 2004) and symmetry in both sexes (Hughes, Harrison & Gallup, 2002; Hughes, Pastizzo & Gallup, 2008). Indeed, Hughes et al. (2009) found that the mere sound of an individuals voice allows people to infer accurate sex-specific body configurations of the speaker (i.e. shoulder-to-hip ratio for men and waist-to-hip ratio for women). Individuals also tend to associate positive personality traits with those who have more attractive voices (Zuckerman & Driver, 1989), and those with attractive voices are thought to be warmer, more likable, honest, dominant, and more likely to be successful (Berry, 1990; Zuckerman & Driver, 1989). Vocal attractiveness is also perceived by the listener to be indicative of positive traits within the personality dimensions of neuroticism and conscientiousness (Zuckerman, Miyake, & Elkin, 1995). There are also distinct sex differences with regards to pitch preferences for opposite-sex voices; masculine, lower-pitch male voices are preferred and seen as dominant by women (Apicella, Feinberg, & Marlow, 2008; Puts, 2006), while men, perceive higher-pitched female voices as sounding more attractive (Collins & Missing, 2003; Feinberg, DeBruine, Jones, & Perrett, 2008; Jones, Feinberg, DeBruine, Little, & Vukovic, 2008).

Several studies have examined the ability of raters to assess the age of others based solely on hearing their voice. Generally, differences between estimated and actual ages based on the voice recordings are small (Lass, Justice, et al., 1982). Naranja and Kushal (1982) showed that raters could accurately determine the age of a child (ages 3 to 5) within one year, on average, of a childs actual age. Likewise, Hummert, Mazloff, and Clark (1999) showed individuals were very accurate at assessing the age of the voices of older adults who were 60 years of age and older. Cerrato et al. (1999) found that raters were able to assign a general age category to a voice using 6-year increments, starting at age 18. Listeners can also easily differentiate voice samples of young adults from adults over the age of 65 (Ptacek & Sander, 1966). When comparing the acoustic cues of vocal aging, older male speakers show significantly longer sentence, word, and diphthong durations and higher mean fundamental frequency compared to younger males, and speaking rate appeared to be the main perceptual cue used to accurately identify age from

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voice (Harnsberger, Shrivastav, Brown, Jr., Rothman, & Hollien, 2006). Age can be estimated just as accurately when the listener is familiar with the language of the speaker as when they are not (Braun & Cerrato, 2000). Apparently individuals can also effectively disguise their voices to influence perceived age; when individuals were asked to manipulate their voices in attempt to sound older than they were, Lass et al., (1982) found that age estimates were consistent with the intended disguise.

Other studies, however, have not found high accuracy for age estimates based on voice. For instance, Neiman and Applegate (1990) showed that listeners are not very good at assigning voices to specific age categories. Similarly, Braun and Cerrato (2000) found that people were able to reliably and accurately assess the age of some voices, but not others. Schotz (2001) further suggested that typical and atypical voices may exist and this determines why listeners can accurately judge some voices (within a ten year span of actual age) and not others. Unlike previous studies, the present study is an attempt to examine patterns of accurate age estimates based on voices across the lifespan when considering the reproductive viability of the speaker as a factor that may be important in making accurate age determinations.

A number of physical and hormonal changes take place in males and females as they age which can influence the sound of their voice. For instance, there is a surge of activational hormones at puberty which influences the transition from a child-sounding voice to a mature, adult voice (Mendes-Laureano et al., 2006). At puberty, a males voice decreases by approximately one octave in pitch as a direct result of greater testosterone exposure (Abitbol, Abitbol, & Abitbol, 1999). Similarly, at puberty, the pitch of a females voice also decreases, but to a lesser degree (i.e. female voices decrease by approximately a fourth of an octave) and this is shaped by estrogen and progesterone (Abitbol et al., 1999). As males age, testosterone levels decline (Ferrini & Barrett-Connor, 1998) and the mean volume and variability in volume of their voices increase with age (Hummert, et al., 1999). As adult females age, the decline of sex hormone activity, especially at menopause, affects a females vocal folds and laryngeal function (Amir & Biron-Shental, 2004). Female voices deepen, maximum phonetic frequency decreases, and vocal range expands, allowing them to hit lower pitches (Linville, 1987). Pitch variability and vocal jitter also have been shown to increase with age (Hummert et al., 1999). Therefore, it is alleged that one reason as to why age can be identified from voice samples is a result of these hormonal influences (Shipp & Hollien, 1969).

From an evolutionary standpoint, the ability to make accurate age assessments of others may be important for detecting potential opposite-sex mates who are reproductively viable, or for detecting same-sex competitors. Because females reproductive phase of their life terminates with menopause and the onset of menopause is largely determined by age, it would not be as reproductively advantageous for men to pursue women who are advanced in age because older women could be no longer fertile or have a shorter remaining reproductive lifespan than younger women. Therefore, it is not only adaptive to easily identify those who are sexually mature (i.e. post-pubertal), but also to easily identify menopausal females who are no longer reproductively viable. We propose that voice is a salient indicator of these developmental changes and can allow for especially accurate assessments of age during these times.

In the present study, the ability of raters to assess age based only on hearing the voices of male and female speakers (whose ages range from 2 to 67) was examined, given each raters own age and sex. We hypothesized that members of both sexes would be more accurate at assessing ages from voices produced by individuals around the age of

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puberty (to identify those who are sexually mature), and would be particularly accurate at assessing the age of females as they approach menopause (e.g. ages 46-55) so as to identify those who are no longer reproductively viable from those who are.

Methods

Voice Samples One hundred and one participants (48 males, 53 females) were recruited from a

small Northeastern liberal arts college (who were faculty, staff, students, or others involved in summer programs at the college) to provide voice samples used for this study. All participants completed a brief demographic questionnaire regarding their sex, age, ethnicity, first language, where they were raised as a child, if they smoke, and whether they had a cold that affected their speech. Only those who indicated English as their native language, did not have a strong or distinct accent that was uncommon to the region, nor had any other features that may have affected the natural sound of their voice (e.g., smoked frequently, had a cold/congestion, poor quality voice recording, etc.) were included as our voice stimuli. Participants then provided a voice sample counting from one to ten. Voices were recorded onto an Apple iPod handheld microphone that was held approximately one inch from the speakers mouths. The mean age of those providing voice samples was 28.97 (SD = 16.94, range = 2 to 67). The majority of participants indicated that they were Caucasian (89%), and the remaining participants were African American (3%), Hispanic (2%), Asian (1%), and that of another or mixed ethnicity (5%). Voice ages were divided into 7 groups for analysis: (1 = 2-9 years old [n =12], 2 = 10-15 years old [n = 8], 3 = 16-22 years old [n= 27], 4 = 23-34 years old [n = 19], 5 = 35-44 years old[n = 11], 6 = 46-55 years old [n = 16], and 7 = age 56 and over [n = 8]). This study was approved by the local Institutional Review Board.

Independent Raters A separate set of 97 participants (37 males, 60 females) were recruited from the college and from people living in the surrounding community, to serve as independent raters to determine the sex and estimate the age of the individuals who provided the voice samples. Raters were given a brief demographic questionnaire regarding their own age, sex, ethnicity, where they were predominantly raised as a child, and were excluded from the study if they had reported any hearing difficulty or if English was not their native language. The mean age of the raters was 27.56 (SD = 12.44, range 17 to 68). Of these raters, 73.2% indicated they were Caucasian, 13.4% were African American, 4.1% were Asian, 3.1% were Hispanic, and the remaining 6.2% were of another or mixed ethnicity. Since there were no child raters, Rater Age Groups were divided into only 4 analogous groups to the Voice Age Groups for analysis, (1 = 16-22 years old [n = 59], 2 = 23-34 years old [n = 16], 3 = 35-45 years old [n = 7], 4 = age 46 and over [n = 15]). Procedure for Raters Each rater listened to thirty counterbalanced voice samples that were of a representative mix of the different age groups of the 101 voice samples. Pilot testing revealed that thirty voice ratings were sufficient to not create fatigue for the raters, as

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opposed to presenting all 101 voice samples obtained. First, raters were asked to identify the sex of the voice. Then they were asked to estimate the exact age of the individuals. Raters made assessments in quiet classrooms or lab settings and the group size for raters varied from 1 to 14. Participants listened to each voice sample once unless there was a request made to hear a voice sample again.

Results

Age Assessment Analysis

Because less random error is present when using mean age estimates for each voice, we considered each voice rating made individually which allowed us to take into account more variability in the ratings, and account for the sex and age of the rater, as well as the sex and age of the person providing the voices sample for each voice rating to generate our mean comparisons. A 2 (Rater Sex) X 2 (Voice Sex) X 7 (Voice Age Groups) X 4 (Rater Age Groups) ANOVA was conducted on the difference scores between estimated and actual ages for each individual rating. There were no main effects for Rater Sex, F(1, 2086) = 0.13, p = .716, Voice Sex, F(1, 2086) = 0.40, p = .527, or Rater Age Group, F(1, 2086) = 1.20, p = .309. However, a main effect for Voice Age Group was found, F(6, 2806) = 72.00, p < .001. Means and standard deviations for differences between age estimates and actual age for each Voice Age Group are presented in Table 1. Tukey post hoc analyses showed that all pairwise group comparisons were significantly different from one another aside from Voice Age Groups 1 and 2, 1 and 3, 2 and 3, and 6 and 7. There was a significant pattern of underestimating the ages of the speakers as the speakers got older, r = -.42, p < .001.

Table 1. Descriptive Statistics for Different Scores and Absolute Different Scores between Age Estimates and the Actual Age for Each Voice Age Group Speakers for Both Sexes

Voice Age Group

Mean Differences from Actual Age

SD Mean Absolute Differences from Actual Age

SD

2-9 0.20 0.83 1.56 0.60

10-15 0.84 0.72 3.48 0.52

16-22 1.71 0.53 6.56 0.39

23-34 -1.89 0.63 9.90 0.46

35-45 -7.29 0.67 12-20 0.48

46-55 -11.18 0.47 14.03 0.34

56+ -11.75 1.55 12.83 1.13

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There was also a significant interaction between Voice Sex and Voice Age Group, for the difference scores between estimated and actual ages for each individual rating, F(6, 2806) = 16.56, p

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Table 2. Sex Differences in Mean Difference Scores between Estimated and Actual Age of Speakers Across All Speaker Age Groups

Voice Age Group

Voice Sex N Mean Age Difference SD t p

2-9 male female

117 174

0.48 -0.07

1.96 2.09

2.27 0.024

10-15 male female

105 175

1.10 1.01

4.54 6.27

0.14 0.888

16-22 male female

340 322

2.86 0.01

8.85 8.56

4.21 < 0.001

23-34 male female

239 219

0.43 -4.90

11.10 11.08

5.13 < 0.001

35-45 male female

175 191

-10.31 -2.31

9.42 13.82

-6.41 < 0.001

46-55 male female

322 358

-14.19 -8.16

13.14 12.10

-6.22 < 0.001

56+ male female

62 105

-10.79 -12.71

12.59 10.91

1.03 0.303

To get a better understanding of accuracy for how many years difference the age

estimates were from the speakers actual age, we conducted a 2 (Rater Sex) X 2 (Voice Sex) X 7 (Voice Age Groups) X 4 (Rater Age Groups) ANOVA on the absolute difference between estimated and actual ages for each individual rating. Similar to difference scores, there was only a main effect for Voice Age Group, F(6, 2806) = 86.88, p < .001. Descriptive statistics for absolute differences between age estimates and actual voice for each Voice Age Group are also presented in Table 1. Tukey post hoc analyses showed that all pairwise group comparisons were significantly different from one another aside from Age Groups 5 and 7 and 6 and 7. As speakers age increased, the amount of years estimated from actual age also increased, r = .44, p < .001. Figure 2 also illustrates how accuracy progressively showed an overall decline as the age of the speakers increased for each Voice Age Group.

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Figure 2. Absolute mean differences of estimated ages from actual ages across different age groups of male and female voices.

There was a significant two-way interaction between Voice Sex and Voice Age

Group for absolute differences between estimated and actual ages, F (6, 2806) = 12.30, p < .001. However, the only significant sex differences between accuracy ratings laid within in the age groups 35-44 and 46-55, with raters showing better accuracy for female voices (see Table 3). There was also a two-way interaction between Voice Age Group and Rater Age Group for absolute differences between estimated and actual age ratings, F(18, 2806) = 1.97, p = .008. Figure 3 illustrates the general decline of accuracy of age estimates as speaker age increased across all rater age groups. Ratings were generally consistent among all rater age groups with the most variability amongst Rater Age Groups occurring in three oldest Voice Age Groups.

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Table 3. Sex Differences in Mean Absolute Difference Scores between Estimated and Actual Age of Speakers Across All Speaker Age Groups

Voice Age Group Voice Sex N Mean Age Difference SD t p

2-9 male female

117 174

1.47 1.43

1.38 1.53

0.30 0.763

10-15 male female

105 175

3.33 4.31

3.26 4.65

-1.91 0.057

16-22 male female

340 322

5.15 6.09

7.75 6.01

-1.74 0.083

23-34 male female

239 219

8.27 10.38

7.39 6.22

-3.29 0.001

35-45 male female

175 191

12.06 11.03

7.03 8.59

1.24 0.215

46-55 male female

322 358

16.50 11.61

10.07 8.84

6.75 < 0.001

56+ male female

62 105

12.85 13.63

10.44 9.70

-0.049 0.625

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Figure 3. Mean differences of estimate ages from actual ages across different rater age groups for each of the voice age groups. Gender Assessment Analysis Table 4 provides the statistics for the percent correct of gender assessment within each speaker age group. A One-Way ANOVA, independent measures analysis showed Voice Age Group had a significant effect on percent of raters who identified the gender of a voice correctly, F(6, 94) = 4.22, p = .001. Tukey post hoc analyses showed that Age Groups of 2-9 (M = 80.96, SD = 5.91) and 10-15 (M = 75.36, SD = 31.67) were not significantly different from one another, but were significantly different from all other groups (see Table 4). When considering Voice Sex and Voice Age Group, the main effect for Voice Group remained, F(6, 87) = 4.69, p < .001, but there was no main effect for Voice Sex, F(1, 87) = .001, p = .999, nor a significant interaction between Voice Age Group and Voice Sex for gender assessment accuracy, F(6, 87) = 1.01, p = .425.

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Table 4. Mean Percent Correct for Accurate Gender Identifications within Each Speaker Age Group

Voice Age Group N Mean SD

2-9 12 80.96 15.91

10-15 8 75.36 31.67

16-22 27 96.74 7.72

23-34 19 95.98 7.88

35-45 11 93.54 11.19

46-55 16 92.76 13.74

56+ 8 93.90 9.96

Discussion

Our data show that raters were the most accurate in determining the age of both male and female speakers for the youngest few age groups (2 to 9-year-olds, 10 to 15-year-olds, 16 to 22-year-olds, and 23 to 34-year- olds), whereby most estimated age ratings differed from the speakers actual ages by less than five years. Across all speaker age groups, raters were the most precise in estimating the childrens ages, closely followed by estimates of adolescents ages. This finding supports Naranja and Kushal (1982) who also showed that raters could accurately determine the age of a child (ages 3 to 5) within one year, on average, of a childs actual age. Accurate assessments of childrens ages may be especially adaptive so as to determine the type of care that is appropriate for children at distinct developmental stages. Furthermore, easily ruling out individuals who are not reproductively mature is also essential for mate choice. As such, our data show that accuracy of age estimates remained consistently high for adolescents, which supports our hypothesis that voice could be a good index to determine the age of those experiencing the onset of reproductive viability. We suspect that the considerable amount of hormonal changes that occur at the onset of puberty, which have profound effects on both male and female voices (Arbitbol et al., 1999), is what makes it easy to identify age based on voice at this developmental stage. Both the surges of testosterone for boys and estrogen and progesterone for females at puberty lower the pitch of the voices (Arbitbol et al., 1999), creating more mature, adult-sounding voices. Although raters were most accurate in identifying the ages of children and adolescents based on their voice, they were the least accurate for correctly identifying the gender of those age groups (as compared to the adult age groups showing well over 90% accuracy rates). Prior to puberty, male and female voices are more similar and distinctions between each gender become more evident only after puberty when hormonal influences change the voice in different ways for each sex (Arbitbol et al.,

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1999; Hagg & Taranger, 2003). Voice is seen as one of the most salient cues of sexual maturity (Hagg & Taranger, 2003). Therefore, it makes sense that the age of a child can be ascertained prior to puberty, whereas gender identification is more difficult.

Raters showed a general trend of underestimating age as speakers got older, and this trend was largely independent of the raters own age or gender. Similarly, when considering the absolute differences between age estimates and speakers actual ages, there was a general decline in the accuracy of ratings as the speakers ages increased. In our ancestral past, survival of individuals to an advanced older age was rare (Campbell & Loy, 2000), so perhaps the ability to discern the age of elderly individuals was less influenced by selection pressures. In addition to hormonal changes, there are also several life experiences, such as how often one uses their voice and various habits (i.e., smoking, singing, being exposed to toxic fumes, fire exhaust, allergens, etc.) which may influence the sound of ones voice as they age, perhaps making the specificity of age detection decline as a speaker ages. It is also possible that older individuals alter their voices to sound younger as a deceptive tactic, and vocal disguise has shown to be an effective means of changing perceived age (Lass et al., 1982). Nonetheless, it is impressive that raters are still able to detect that these individuals are older despite being possibly masked by several experiential influences.

Furthermore, in support of our hypothesis, age estimates made for female voices during the menopausal years (age group 46-54), were more accurate than they were for male voices, which suggests that people may be more sensitive to the vocal cues associated with menopause. Several studies have shown that substantial vocal changes occur during menopause (Amir & Biron-Shental, 2004; Schneider, van Trotsenburg, Hanke, Bigenzahn, & Huber, 2004) which may be the proximate reason as to why raters could accurately discern females age at that time. Additionally, our findings also revealed that raters were more accurate in estimating the ages of female speakers who were in the 23-34 age group as compared to males, suggesting that there is also sensitivity to female voices for not only developmental stages of declining reproductive viability, but also for detecting heightened reproductive/fertile years.

There was consistency in estimations among all rater age groups for each of the speaker age groups with the exception of the oldest few speaker age groups. For this eldest voice age group, there appeared to be a same peer-group advantage; the oldest rater age group was the most accurate at assessing the ages of the oldest voice age group. Perhaps this is the case because they are more familiar with and have greater exposure to the vocal changes that occur with advancing age. Older male voices tend to have increased volume and variability in volume, and older female voices tend to have lower pitch and voice quality (Amir & Biron-Shental, 2004; Hummert, et al., 1999; Linville, 1987). Our findings may have been affected by the fact that the distribution of raters ages was not exactly proportionate to the distribution of our speakers ages. As such, it would be interesting to also include children and/or adolescents as raters. Previous studies have shown that judgments of voice attractiveness change during the advent of sexual maturity (Saxton, Debruine, Jones, Little, & Roberts, 2009), and perhaps this may also be the case for the ability to assess sexual maturity and perceived age based on voice. Furthermore, the division of our age groups was based upon developmental decades we felt were distinct to reproductive viability, and perhaps using other age divisions would yield somewhat different findings. However, it did not appear that the age divisions used showed profound effects on ratings since there was a general lack of

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peer group advantage when making ratings. It is also possible that the advantage in identifying the age of women in the older age groups (e.g., menopausal-age women) could have occurred due to the fact that this age would correspond with the age of many of the participants mothers, thereby exposure to such voices would allow for better assessment. Other factors that could influence age ratings for female voices include their temporal location in menstrual cycle, contraceptive use, and the use of hormone treatments post-menopause, as previous studies have shown that each of these factors can influence vocal characteristics in women (Amir & Biron-Shental, 2004; Amir, Kishon-Rabin, & Muchnik, 2002; Meurer, Garcez, Corleta, & Capp, 2007; Pipitone & Gallup, 2008; Schneider, et al., 2004). Future investigations could also document exact information of the actual developmental state (i.e., records of menarche or menopause) of the adolescents and menopausal-aged female participants, as this was a limitation in our study. Objective acoustic features of the voices used in rater age assessment could also be examined. It would also be interesting to measure any physiological responses elicited from hearing voices across different ages so as to assess differential arousal responses toward voices of particular reproductive ages. Furthermore, future studies could also examine how important voice is as a cue for reproductive viability in comparison to other various physical traits used to make such assessments.

Received July 16, 2010; First revision received October 17, 2010; Second revision received November 9, 2010; Accepted November 10, 2010

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Making Age Assessments Based on Voice

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