Right for the Job or Opposites Atract? Exploring
Cross-Generational User Experiences with “Younger” and “Older”
Voice Assistants
Yuto Sawa
Tokyo Institute of Technology, sawa.y.ac@m.titech.ac.jp
Julia Keckeis
Tu Berlin, j.keckeis.ofcial@gmail.com
Katie Seaborn∗
Tokyo Institute of Technology, seaborn.k.aa@m.titech.ac.jp
ABSTRACT

1

Eforts to diversify the design of virtual assistants (VAs) are inspiring new work on voice as a factor of user experience (UX).
One trajectory is voice agedness. The Computers are Social Actors(CASA) paradigm suggests that humanlike computer agents areperceived in line with models of people. Notably, work on ageism
and similarity attraction theory would predict that agents with“younger” and “older” voices may be treated diferently. As a frst
efort, we explored the UX of a novel “older adult” text-to-speech(TTS) system. Younger (n=16) and older (n=18) people evaluateda VA that switched voices (“young” and “old”) in a storytelling
activity. While most preferred the VA’s younger-sounding voices
on most measures, all tended to rate the “elder” VA as more appropriate within the storytelling context, challenging expectations
based on similarity-attractiveness theory. This work provides initialclues about the relationship between age characteristics and age
stereotypes for voice UX.

Studying the user experience (UX) of voice-based virtual assistants(VAs) has become important with the update of this technology
globally [17]. VAs are used in a variety of applications, including
information search, entertainment, online shopping, smart home
controls, e.g., lighting [13], and more recently complex, conversational interactions based on the development of natural language
processing (NLP) technologies. Factors that infuence voice UX include the VA’s accent, appearance, and social characteristics, suchas genderedness and agedness, as well as user characteristics andmental models of the world [18]. For example, studies have shownthat older users may have diferent preferences and needs wheninteracting with a voice assistant compared to younger or middleaged users [8]. Nevertheless, very little research has involved older
adults [17] and even fewer have explored VAs voiced by “older
adult” text-to-speech (TTS) systems [8, 17]. This is important because stereotypes about specifc human social groups can also afectevaluations of voice-based agents [14]. With “aged” voices, ageism
may play a role. Ageism refers to limited and/or negative age-based
attitudes and discriminatory behaviour, especially towards older
age groups; it is considered one of the most discriminatory attitudes,
after racism and sexism [15]. Diversifying the voices of VAs should
include diversifying the agedness of these voices, but we should
also consider whether and to what degree attitudes like ageism
infuence UX.
To illuminate and clarify the efects of the agedness of VA
voice, we conducted a comparative user study, involving a crossgenerational sample of younger and older adult participants interactive with a suite of four “younger” and “older” voiced VAs.
We asked: RQ1: Does voice UX difer by voice agedness and basedon participant age? And RQ2: Do attitudes toward age, i.e., ageism,
afect the voice UX based on voice agedness? As an in-progress work,
we contribute an initial understanding of factors related to VA anduser age that can infuence voice UX. This is important not only
for the design and development of VAs, but also for understanding
human attitudes towards age.

CCS CONCEPTS• Human-centered computing; • Human computer interaction (HCI);KEYWORDS
Voice assistants, Virtual assistants, Voice interface, Older adults,
Ageism, Computer voice, Voice UX, User experience
ACM Reference Format:
Yuto Sawa, Tokyo Institute of Technology, sawa.y.ac@m.titech.ac.jp, Julia
Keckeis, Tu Berlin, j.keckeis.ofcial@gmail.com, Katie Seaborn, and Tokyo
Institute of Technology, seaborn.k.aa@m.titech.ac.jp. 2023. Right for the Job
or Opposites Attract? Exploring Cross-Generational User Experiences with
“Younger” and “Older” Voice Assistants. In Designing Interactive Systems
Conference (DIS Companion ’23), July 10–14, 2023, Pittsburgh, PA, USA. ACM,
New York, NY, USA, 4 pages. https://doi.org/10.1145/3563703.3596642∗ Corresponding author.

INTRODUCTION

This work is licensed under a Creative Commons Attribution-Share Alike
International 4.0 License.

2 THEORETICAL BACKGROUND
2.1 Vocal Computers Are Social Actors

DIS Companion ’23, July 10–14, 2023, Pittsburgh, PA, USA© 2023 Copyright held by the owner/author(s).
ACM ISBN 978-1-4503-9898-5/23/07.
https://doi.org/10.1145/3563703.3596642

According to the Computer Are Social Actors (CASA) paradigm,
we may unrefexively apply our social responses to people to computer agents we are interacting with that have humanlike cues [14].

160

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Yuto Sawa et al.

We unconsciously read and react to cues related to social factors
embedded in agent appearance and behaviour, such as appearance,
voice, and gender, and classify the agent as a social entity [14].
People can recognize diferent voice “ages” based on features ofcomputer-generated speech manipulated through speech rate andpitch [11]. In this early work, we evaluate a novel TTS created fromscratch with the voices of older adults, rather than modulating ofexisting voice stimuli.

Thirty-four people participated. Sixteen were young people (9 men,
7 women, and none of another gender), all university students andworking adults in their 20s, recruited by the frst author. Eighteen
were older adults aged 60+ (9 men, 9 women, and none of anothergender) recruited from Ota-ku silver centre. Participants were compensated at 1,200 yen/hr. This study was approved by the Ethical
Review Committee at Tokyo Institute of Technology.

2.2

3.2

Similarity-Attraction Theory and Voice UX

According to similarity-attraction theory (SAT) [4], we evaluate
others as more attractive when they ha attributes similar to ourown. This can afect UX-relevant factors such as liking [7], familiarity [1], and satisfaction [5]. In a preprint, Yücel and Rızvanoğlu1changed the pitch and speed of a VA’s speech to create “younger”
and “older” versions. They reported that older adult participantspreferred the VA with high emotional empathy. However, the “older
adult” VA was not signifcantly better than the younger-sounding
VA at increasing feelings of trust and perceived usefulness. In thisresearch, we decided to investigate the same measures of trust,likability, familiarity, satisfaction, and humanlikeness of two types
of VAs: one that sounds older and one that sounds younger. We
also drew on similarity-attraction theory but extended this work
to include younger and older adult participants. Given this, wehypothesized that:
H1-1: The older-sounding VA will be more positively evaluated byolder adults than younger adults.
H1-2: The younger-sounding VA will be more positively evaluatedby younger adults than older adults.

2.3

Voice and Ageism

Ageism is a complex prejudice that includes both positive and negative stereotypes about older adults [15]. Palmore [15] defned eight
positive stereotypes—Kindness, Wisdom, Reliability, Wealth, Political Power, Freedom, Eternal Youth, and Happiness—and nine
negative stereotypes—Illness, Impotence, Ugliness, Mental Decline,
Uselessness, Isolation, Poverty, and Depression. Ageism is also expressed in both benevolent and hostile treatment [6]. Measuring
ageism attitudes from multiple perspectives is useful for studying
how stereotypes afect ageist behavior [10]. Horhota et al. [10]found that level of acceptance when viewing benevolent and hostile ageism behavior with older adults changed depending on theparticipant’s age and the relationship between the participant andthe target. Based on this, we hypothesized:
H2: Ageism attitudes will be negatively correlated with positive
impressions of a VA that sounds “old.”

3

METHODS

In a comparative user study, each participant evaluated two voicesof a VA storyteller in a within-subjects design (“younger” and“older” VAs). The between-subjects factor was participant age group(younger and older adult).

3.1

Participants

System Design

We trained an “older adult” TTS with the phonetic features ofan older woman’s recorded voice. A sample is provided in the
Supplementary Materials. For the “younger” voice, we used the jaJP-Wavenet-B feminine TTS by Google2 . Two manipulation checks
were performed in-lab, confrming the expected gender and age
attributions for each voice. A web-based Wizard of Oz app was
created using voice clips generated by the TTS models for theactivity (3.3). The VAs were identical and carried out the activity inthe same exact way except for the agedness of the voice, i.e., the
TTS used.

3.3

Activity

We created a storytelling activity to inspire dreams before bed. To
select the “dream” stories, we conducted an online survey on people’s last dream, most memorable dream, and dreams they would
like to have. Forty-nine people (21 men, 28 women, none of anothergender; 25 aged 18-44 and 24 aged 65+) responded. A thematic
analysis [3] by the frst author of the qualitative items revealed thehigh-level themes of "characters in the dream," "dream locations,"
and "own actions in the dream,” with sub-themes for each theme,
respectively: "I alone," "pets," "family," and "people I admire"; "sightseeing spots," "sky," and "daily life"; and "eating," "conversation,""going out," and "romantic interactions." The ranking of multiplechoice items was "fying (17)," "being in nature (16)," "traveling (15),""food (15)," "meeting famous people (11)," "money (7)," and "interacting with animals (6)." Based on this, the frst author selected six
stories, ∼5 mins. each, from the Aozora Bunko (Blue Sky Library)3 .
Participants conversed with the VA to narrow the stories down in
two ways: presence of family or an animal. The VA then read thestory aloud. The activity was refned through lab pilot testing.

3.4

Procedure and Context

Each participant had two sessions with the VA in our lab, sitting at
a table and using a PC displaying the VA system over Zoom. One
researcher hosted while another controlled the VA from another
PC. The voice of the VA was switched from “young” to “old” in acounterbalanced fashion across participants. Each session involved
one story (3.3). Then the VA asked the participant to fll out thequestionnaire. To switch voices between sessions, the VA explainedthat they were trying on a new story voice. The study took between
40-60 mins. A 10-15-min. break was allowed between sessions.

2 https://cloud.google.com/text-to-speech/docs/voices

1 https://doi.org/10.21203/rs.3.rs-1905540/v1

3 https://www.aozora.gr.jp161

Right for the Job or Opposites Atract? Exploring Cross-Generational User Experiences with “Younger” and“Older” Voice Assistants

DIS Companion ’23, July 10–14, 2023, Pitsburgh, PA, USA

Table 1: Descriptive statistics for “young” and “old” voices by participant age group (younger and older adults)
MeasureSatisfaction
Trust
Familiarity
Likability
Anthropomorphism

3.5

Young Adults“Young” Voice
5.4
5.6
4.2
3.6
2.6

Older Adults“Old” Voice
4.8
5.0
4.8
3.4
2.9

“Young” Voice
4.7
5.1
4.6
3.6
2.7

“Old” Voice
3.7
4.9
4.0
3.1
2.4

(“young” n=7, “old” n=1), coldness (“young” n=5, “old” n=0), feeling relaxed (“young” n=1, “old” n=9), and compatibility with theactivity (“young” n=0, “old” n=7). Younger and older participants
did not difer in their impressions of the “young” and “old” voices interms of statistical signifcance (Table 1). Altogether, given the lackof statistically signifcant diferences at the intersection of voice
age and participant age, we must reject H1-1 and H1-2. Likewise,
no statistically signifcant correlations were found for ageism byvoice age, so we reject H2. In short, it appears that voice age alone
and/or individual factors beyond ageism attitudes were infuential.

Instruments and Data Collection

After each session, participants flled out a questionnaire and wereinterviewed about their impressions of the VA.
3.5.1 Satisfaction, Trust, and Familiarity. Satisfaction, trust, andfamiliarity were measured using single-item 7-point Likert scales
from work on a home VA [12].
3.5.2 Likability and Anthropomorphism. Likability (� = .982) andanthropomorphism (� = .937) were measured using the Godspeedquestionnaire [2]. Each was comprised of fve items measured on
5-point Likert scales. Similar to other VA research, e.g., [19], we
modifed the items to make them meaningful for VAs. A lab pilot
survey with seven people confrmed the changes.

5

DISCUSSION

3.5.3 Ageism Atitudes. Ageism was measured with the AAS-JP[16], a Japanese translation of the Ambivalent Ageism Scale (AAS)[6]. 7-point Likert scales were used to capture responses to 13-item
benevolent (� = .703) and 4-item hostile subscales (� = .369). Given
the low Cronbach’s alpha, i.e., internal consistency, for the hostile
subscale, we relied on the benevolent one.

Against expectations, people’s impressions of and UX with the VA
in the two “young” and “old” age “modes” were not linked to theirown age or attitudes towards ageism. Instead, people tended tofavour the “young” voice as a more familiar and higher quality TTS,
while at the same time favouring the “old” voice as a warm “elder”
storyteller most appropriate for the activity and somehow able toexpress relaxation and greater afect.

3.5.4 Brief Interview. Participants were interviewed at the end of
the study. They were asked: “What is your general impression of
the VA?”

5.1

3.6

Data Analysis

Descriptive statistics and normality checks via the Shapiro-Wilk
test were conducted. Refexive thematic analysis [3] was used by the
frst author for the qualitative data. Pearson correlations and t-tests
were used to compare all measures according to the hypotheses,
i.e., based on voice age group and participant age group. However,in cases of nonnormal distributions, nonparametric alternatives,
including Kendall’s rank correlations and Mann-Whitney U tests,
were used.

4

RESULTS

Satisfaction with the “younger” voice (5.1 ± 1.4) was statisticallysignifcantly higher than the “older” voice (4.24 ± 1.71), p = .017.
No other statistically signifcant diferences were found. Thematic
analysis revealed a range of impressions from the quality of thevoices, characteristics of the VA, user attitudes, and activity UX.
Comparisons of the quantifed themes revealed statistically signifcant diferences by voice age for speed, loudness, pronunciation, smoothness, lack of afect, coldness, compatibility with theactivity, familiarity, and ease of listening (Supplementary Table1). These results favoured the “young” voice, except lack of afect162

Age and Agedness in Voice and byGeneration

Younger and older participants were more satisfed with the technical quality of the young voice. Yet, in contrast to the breaking
results of Yücel and Rızvanoğlu4 , our older adult participants did
not have a strong preference for younger-sounding VAs. Rather,
participants of all ages found the “elder” VA to be more appropriate
for the task of conducting the storytelling activity, challenging the
expectations of similarity-attraction theory. One reason could bean efect of complementary-attraction, where we prefer others withpersonality cues complementary to our own [9]. Future work will
have to explore how various UX factors relate to personality cues.
Another possibility is a link between the activity and the age ofthe voice. Specifcally, we may expect elders in our lives, such as a
grandparent, reading a story to us before bed. This may also link topositive yet still stereotyped attitudes of age roles. Future work may
include other measures of age(ist) attitudes and interview questions
on the connection between task and agent.

4 https://doi.org/10.21203/rs.3.rs-1905540/v1

DIS Companion ’23, July 10–14, 2023, Pitsburgh, PA, USA

5.2

Yuto Sawa et al.

Ain’t Nothing But a Number? Ageism
Attitudes and Older-Sounding Voices

We could not link ageism to voice UX with the older-sounding
VA. Notably, we had to rely on the benevolent subscale. The AASJP was validated for internal consistency with older adults [16]but the original AAS involved young and middle-aged people [6].
Our results may be a matter of our sample size or generational
diferences in ageism attitudes.

5.3

Limitations and Future Work

Technical quality of the TTSs may have infuenced results; wewill improve the “older adult” TTS by training the models withlonger voice recordings. We will also increase the sample size when
conducting the full longitudinal study.

6

CONCLUSION

Voice UX can be infuenced by the agedness of the VA and theuser’s age group. When developing and deploying VAs , we should
consider the perceived age of the voice, the voice characteristics
that can infuence it, and the attributes of the user. Future work
will need to replicate and extend these results for other scenariosand socio-linguistic contexts.

ACKNOWLEDGMENTS
This work was funded by a Japan Society for the Promotion of
Science (JSPS) Grants-in-Aid for Early Career Scientists (KAKENHI
WAKATE) grant (no. 21K18005) and the Tokyo Tech Young Scienceand Engineering Researchers Program (YSEP). We also thank theFonobono Research Institute and all members of the Aspire Lab forsupporting this work.

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