Can a gender-ambiguous voice reduce gender stereotypes inhuman-robot interactions?
Ilaria Torre1 , Erik Lagerstedt2 , Nathaniel Dennler3 , Katie Seaborn4 , Iolanda Leite1 & Éva Székely1
Abstract— When deploying robots, its physical characteristics, role, and tasks are often fixed. Such factors can also be
associated with gender stereotypes among humans, which thentransfer to the robot. One factor that can induce gendering but
is comparatively easy to change is the robot’s voice. Designingvoice in a way that interferes with fixed factors might therefore
be a way reduce gender stereotypes in human-robot interaction
contexts. To this end, we have conducted a video-based online
study to investigate how factors that might inspire gendering ofa robot interact. In particular, we investigated how giving therobot a gender-ambiguous voice can affect perception of therobot. We compared assessments (n=111) of videos in whicha robot’s body presentation and occupation mis/matched withhuman gender stereotypes. We found evidence that a genderambiguous voice can reduce gendering of a robot endowed withstereotypically feminine or masculine attributes. The results caninform more just robot design while opening new questions
regarding the phenomenon of robot gendering.

I. I NTRODUCTION
Social robots are meant to be deployed in a variety ofplaces to complete various tasks, such as helping in schools,
the home, hospitals, factories, and so on. When investigating the effect of certain robot characteristics on humanperception and behaviour, the factors of the embodimentof the robot, i.e., which robotic platform will be used,
and its assigned task and context of use are very often
fixed. For example, if we want to investigate whether acertain characteristic influences children’s engagement in alearning activity, we must work with a fixed robot (whichever
robot we have available or have deemed suitable for theinvestigation) in a fixed context (the learning activity).
The fixed factors of robot embodiment and context of use
can affect how the robot is perceived, particularly if they
are based in human social identities and/or related to humangender stereotypes [1]. Notably, previous work has found thatpeople transfer gender stereotypes from the human world toartificial agents, including virtual avatars [2], computers [3],and robots [4]. For example, “male” robots were perceived as
more suitable for stereotypically “male” tasks, e.g., repairing
technical devices, guarding a house, while “female” robots
were perceived as more suitable for stereotypically “female”
tasks, e.g., tasks related to household and care services [4],1 School of Electrical Engineering and Computer Science, KTH
Royal Institute of Technology, Sweden [ilariat, iolanda,

szekely]@kth.se
2 SchoolofInformatics,

UniversityofSkövde,Swedenerik.lagerstedt@his.se
3 Department of Computer Science, University of Southern California,
USA dennler@usc.edu
4 Department of Industrial Engineering and Economics, Tokyo Instituteof Technology, Japan seaborn.k.aa@m.titech.ac.jp[5]. This poses a dilemma. On the one hand, ensuring thatrobots are matched in terms of perceived gender and task
suitability might reduce the potential for Uncanny Valley
effects and increase trust [6]. On the other, explicitly mismatching gender cues might reduce gender stereotypes overtime, i.e., the robot as a debiasing tool [7], [8].
Since direct experiences with social robots are still rareamong the general public, we have an opportunity to act now
and study how human attributes are (or could be) translated
to robots so as to minimise deception while ensuring understandability and optimal user experience (UX) [9]. Any initialattributes ascribed to robots that are associated with humanstereotypes can then be incrementally challenged as they are
identified and before robots become more common used bya diversity of people. To this end, we must understand how
human social identities are shaped during interactions withthe robot and how different human social norms and cues
interact with each other. However, this may not be straightforward. For example, people may perceive robots morefavourably and be more willing to comply with robots when
the robots behave appropriately for their task [10]. Moreover,even though stereotypes regarding gender and occupation
can impact perceived competence and trust in human actors,
these phenomena do not always transfer to gendered robots[11]. We must further study these phenomena with a varietyof complexity to be able to capture higher order effects.
One characteristic relevant to gender attribution is robot
voice [12], [13]. Unlike the robot platform, i.e., body andprogrammable actions, and the application context, voice ismore “free” in the sense that it can be chosen for the robot
independently of any hardware or application constraints. As
before, this raises a dilemma. First, we must ensure thatthe voice is appropriate for the robot (see e.g. [8], [14]).
This is sometimes assumed to mean that the voice should be
congruent with other physical and social characteristics of therobot, such as its size and shape [8], [14]. However, voice
could be used to challenge the gender stereotypes afforded by
the aforementioned “fixed” robot factors [15]. As yet, little
work in HRI has explored these tensions and potentialities.
Reducing stereotypes does not necessarily mean adding
the “opposite” attribute, e.g., a masculine voice for a feminine body. This risks rein/en/forcing a binary, cisnormative
perspective of gender and the heterosexuality matrix [16],where there are only men and women who are “opposites,”assumed to be heterosexual, and fixed in terms of sex1 ,gender, and sexual identities. An alternative is mixing and
1 We distinguish sex as the biological and gender as the social [17].

matching attributes to find something new, while avoiding
Uncanny Valley territory [6]. Here, we explore one potential
strategy: gender-neutral voice [13]. We selected a genderambiguous synthetic voice to serve as the voice of a robot
mis/matched in terms of gender stereotypes in appearance
and application context [4]. We considered whether thevoice was perceived as gendered, ambiguous, and/or genderneutral, as well as what factors of the robot embodiment—
body or role within the context—were influential. We asked:
Does a robot with a gender-ambiguous voice influence
perceptions of its gender, uncanniness, and trustworthinessin embodiments matched or mismatched according to humangender stereotypes? We extend previous methodologies forgender and robot embodiment as well as contribute novel
findings on the utility of gender-neutral voice.
II. R ELATED W ORK
A. Gender Neutrality in Robots
The notion of gender neutrality in robots and other agentshas a relatively long but complicated history in HRI [12].
Seaborn and Pennefather [12] conducted a rapid review of therobotics literature, finding that voice was a key factor in modulating perceptions of robot gender. Even so, the literature
has so far been limited in important ways, notably insufficient
theorizing on gender and designing for gender perceptions
in robots; assumptions of gender neutrality and a lack ofmanipulation checks; and limitations in reporting, preventing
a clear consensus on design and experiential findings related
to voice, body, role, application context, methodology, user
perceptions, and so on. This work highlights a need forincreased rigour and a concerted focus on gender neutrality,
which we have attempted to address.
Seaborn and Pennefather [12] and Sutton [18] also raised
the question of how we frame and approach gender conceptually, methodologically, and practically. Notably, we can distinguish robots that are gender ambiguous, i.e., androgynous
and/or having a mixture of masculine, feminine, or othergender characteristics, from those that are gender neutral,i.e., agender or genderless, effectively not stimulating gender
perceptions. At present, the relationship between these twoconcepts for robot voice is unclear. We begin by asking
RQ1: Does a gender-ambiguous voice influence genderingof robots, or is it gender neutral? Using a gender-ambiguous
voice with a robot gendered in body and occupational context
might reduce the overall gendering of the robot by people.
Still, an “ambiguous” voice, i.e., one with a mixture of gender attributes drawn from real people and modified computationally, might instead lead to stronger gender attributions.
As Sutton [18] points out, people are primed to gender evenwhen voice is fundamentally, “objectively” genderless. With
this in mind, we hypothesize H1: A gender-ambiguous voice
will influence the gendering of robots.
B. Mis/matching Gender Stereotypes
We anchored our work on the research design of Eyssel etal. [4], discussed above. However, other work has considered
whether and how we should align human stereotypes about

gender with robotic embodiments. Tay et al. [5] had peopleinteract with a robot in two different gender-stereotyped roles(healthcare as feminine and security as masculine). They
found that people had more positive perceptions of the robot
when its perceived gender matched the gender stereotype
associated with the role. Aşkın et al. [19] showed that actions
stereotypically perceived as feminine or masculine led toperceptions of a pilot-deemed gender-neutral robot as having
the corresponding gender. Moreover, this effect was stronger
when people were asked to consider society’s view rather
than their own. In contrast, Bryant et al. [11] found noeffect of perceived robot gender on perceived occupational
competency and trust towards the robot.
Thus, results so far are mixed. Additionally, we expect aninfluence of the voice, and we are not sure how ambiguous
voices will be interpreted when other features of the robot’s
embodiment are gendered. We therefore asked RQ2: Doesa gender-ambiguous voice influence gendering of robots
that are matched and mismatched in terms of gender bodypresentation and occupation stereotypes? This leads us to
two complementary hypotheses. First, we may expect the
body and application context to influence gendering of thevoice, even if the voice is gender neutral or ambiguous,e.g., [13], [19], or H2a: Stereotypically matched body andoccupation will lead to stronger attributions of gender tothe voiced robot compared to stereotypically mismatching
body and occupation, i.e., complementarity in gender role
stereotypes and body gender perceptions. Second, we would
expect the robot to be gendered in the absence of a voice,e.g., [4], [5], or H2b: Stereotypically matched body andtask context will lead to stronger attributions of gender tothe unvoiced robot compared to stereotypically mismatching
body and task context, i.e., complementarity in gender role
stereotypes and body gender perceptions.
C. Voice Priming Effects
Voice gendering can greatly influence other gender perceptions [12], [13], [18]. Given this, we also wondered,mis/matching aside, RQ3: Does listening to a genderambiguous voice lead to priming gendering effects? As per
the literature, we would expect H3: The gendering of thevoice alone will influence the gendering of the robot andvoice combination. To evaluate this, we included a voice-only
session before the main study featuring a context irrelevantto those used in the main study, i.e., reading an encyclopedia
entry. We then asked participants to evaluate their perceptionsof the voice in terms of gender. This also acted as a post-pilot
study manipulation check.
D. Uncanny Gendering
Mismatching gender cues and stereotypes can lead tofeelings of uncanniness towards the robot [4], [6]. As PaetzelPrüsmann et al. [6] found, robots perceived as gendered andassigned to roles matching the societal stereotypes associatedwith those genders rendered Uncanny Valley effects null.
Still, we should avoid relying on stereotypes because of therisk for interactions with such robots to reify such limited

and negative perspectives in people’s minds [20], [21]. Whatremains understudied is whether and to what extent feelingsof uncanniness occur on gender mismatching. We therefore
asked RQ4: Is there an Uncanny Valley effect when body andapplication context do not match? Pointedly, we sought toconfirm or refute H4: Stereotyped mismatching of robot bodyand occupation will lead to higher ratings of uncanniness,trust, and generally less positive evaluations.
While many of our questions and hypotheses link to an
expectation of stereotyped responses, we remained hopefulthat deploying a gender-ambiguous voice could positively
disrupt and shift perceptions. The main focus of this work
was to evaluate the potential gender neutrality of a genderambiguous voice for robotic embodiments that may (ormay not) be gendered. We thus lean on and extend theliterature on robot design and deployment for contexts inwhich participant gendering of the robot could occur.
III. P ILOT STUDY
We conducted a pilot study to choose a gender-ambiguous
voice for the main study. The Text-To-Speech (TTS) community has recently started to develop voices that could
be considered gender neutral or gender ambiguous [22]–[24]. We selected an industry-standard voice from a setof 6 English-speaking voices for Amazon Polly [25]. We
used “Kendra” with a lowered pitch such that the averagefundamental frequency (f0) was ∼135Hz. We determinedthat all Polly voices were highly intelligible and “Kendra”
at this pitch was perceived as ambiguously gendered. We
also generated 3 novel voices based on state-of-the-art TTS
technologies. A multi-speaker version of the sequence-tosequence neural TTS engine Tacotron 2 [26] was used totrain a voice on two large TTS corpora (female [27], male[28]). A gender-ambiguous timbre can be achieved by using aweighted average of speaker embeddings at inference. Threedifferent combinations of weights (f0.3/m0.3; f0.45/m0.4;
f0.65/m0.55) were selected for evaluation using a Speech
Gender Recognition (SGR) network, similar to the one in[29]2 .
A. Method
The study was conducted on the crowd-sourcing platform
Prolific. Participants listened to 10 sentences synthesisedwith each of the 4 candidate voices in randomised order.
They indicated their perception of voice gender using four
Likert items, ranging from 1 (= strongly disagree) to 5 (=strongly agree): “Feminine,” “Masculine,” “Agender,” and“Ambiguous”. This was to allow for more nuanced ratingsof gender, going beyond the binary (e.g., a male-female continuum), as one voice (or one robot, or one person, for thatmatter) can have masculine, feminine, and other attributes
at the same time. We provided definitions for “Agender”
and “Ambiguous”, as follows: “By ‘Ambiguous’, we meanthat the voice sounds neutral or androgynous; by ‘Agender’,we mean that the voice does not seem to have a gender at
2 The full description of this novel approach is beyond the scope of thispaper, and is under review elsewhere.

Fig. 1: Gendering of the candidate gender-ambiguous voices.

all”. To rule out any priming effects due to presenting one
scale before the other, we also showed half the participants
the “Feminine” scale first, and half the participants the“Masculine” scale first. The sentences were taken from the
TIMIT corpus [30], which consists of emotionally neutral
utterances [31], and were passed through a gender bias
detection tool to ensure they were free of textual bias3 . The
set of stimuli also included 4 attention checks, which were2 unambiguously feminine voices (Amazon Polly “Kendra”
with f0 set at 150 Hz and the Tacotron TTS using only thefemale speaker embedding) and 2 unambiguously masculine
voices (Amazon Polly “Kendra” with f0 set at 120 Hz and
the Tacotron TTS using only the male speaker embedding).
Thus, each participant listened to 44 sentences in total.
We recruited 62 participants (30 identified as female, 31
as male, none as non-binary or preferring to self-describe,and 1 preferred not to say), aged 19–48 (median=25). The
experiment took ∼20 minutes and participants were paid
£3.00. The study was run in accordance with the ethicalguidelines from KTH Royal Institute of Technology.
B. Data Analysis and Results
Two participants failed 1+ attention check and were removed from the analyses, leading to a data set from 60
participants. Analyses were conducted in R (v4.2.1). As
per Figure 1, there was more variation for the voices wedesigned (called “E 3030”, “E 4540”, “E 6555”), whereas
the Amazon Polly voice (“K 135”) was more consistently
neutral across all scales. Thus, we selected this voice for themain study.
IV. M AIN STUDY
After finding our “gender ambiguous” voice, we conducted
a video-based study to see whether adding this voice tomasculine and feminine robots that did or did not conformto human gender stereotypes (in terms of appearance andoccupation) would influence the its perceived gender. Our
protocol was registered before data collection on OSF4 .
3 https://www.appcast.io/gender-bias-decoder
4 https://osf.io/epy83

A. Method

Fig. 2: Feminine (left) and masculine (right) robot conditions.
We created videos of the Furhat robot in different conditions. We chose this robot due to its manipulability, e.g.,modifications of its face while keeping the rest of the body
constant. For the feminine version of the robot, we chosethe “Fedora” face and placed a long-haired brunette wig
on the robot’s head. For the masculine version, we chosethe “Marty” face with a beanie hat (see Figure 2). Previous
studies have shown that such cues can induce gendering ofrobots, even when shown on screen [32]. For the stereotypical occupations, we selected child tutor as stereotypicallyfeminine, and security guard as stereotypically masculine(as per [4], [5]). We generated two utterances from theselected gender-ambiguous voice: “Hi, my name is Sam. I
was developed by a Swedish robotics company and my job isto tutor children after school” and “Hi, my name is Sam. I
was developed by a Swedish robotics company and my job isto guard people’s homes.” The robot’s name was chosen asa gender-neutral English name. We mixed and matched thevideos and utterances to create two sets of two videos thatmatched or mismatched based on human gender stereotypes
Finally, we created voice-less versions of the videos with thesentences written as subtitles. Thus, we had 8 video stimuli.
Participants were recruited on Prolific. After agreeing toparticipate, they listened to the TTS voice “reading” a text
adapted from the Encyclopaedia Britannica entry for “robot”
and rated its genderedness with the pilot study scales. They
then answered two attention check questions. Next, they
watched the first block of video stimuli (either voiced or
unvoiced) and rated each video using 9-point Likert items
in terms of gender and Uncanny Valley-related perceptions[33]: likeability, trustworthiness, uncanniness, creepiness,scariness, eeriness. Then they answered two questions on
their understanding and impressions of robots in general,
which served as a distractor task. Then they watched andrated the second block of video stimuli. The study ended
with a demographics questionnaire (gender and age) and aquestion on whether they had technical issues or any othercomments. The experiment took ∼20 minutes.
We recruited 120 people. 9 failed the attention checks or
had technical issues and were removed. Of the remaining
111, aged 18–53 years old (median=23), 56 identified as
male, 50 as female, 3 as non-binary, and 2 preferred not
to say. They were paid £3.00. The study conformed to the
ethical guidelines of KTH Royal Institute of Technology.

Fig. 3: Average gender ratings divided by voicing (presenceor absence of the gender-ambiguous TTS voice) and robot
gender manipulations (appearance and occupation).

B. Results
As a first manipulation check, we looked at how people
perceived our gender-ambiguous voice, which they listened
to prior to watching the video stimuli. A Kruskal-Wallis rank
sum test showed that people perceived the voice differently
in terms of gender (χ2 (3) = 125.11, p < .001). Pairwise
post-hoc comparisons using the Wilcoxon rank sum testwith continuity correction showed that, on average, people
rated the voice as being more “agender” than “ambiguous”(p = .004); more “feminine” than “agender” (p < .001),“ambiguous” (p < .001), and “masculine” (p < .001);
and less “masculine” than “agender” (p < .001) and “ambiguous” (p < .001). Thus, it seems that, overall, people
perceived the voice as more “feminine”, even though theratings centre around the “neutral” label in the Likert items(see summary statistics in Table I).
GenderAgender

Ambiguous

Feminine

Masculine

Voice
Fem. body
Masc. bodyFem. task
Masc. task

2.72 (1.09)
2.97 (1.00)
3.12 (0.95)
3.06 (1.01)
3.03 (0.94)

3.14 (1.10)
2.92 (0.86)
3.11 (0.83)
3.00 (0.87)
3.03 (0.83)

3.84 (0.93)
3.54 (1.00)
2.59 (0.88)
3.04 (1.04)
3.09 (1.06)

2.17 (0.88)
2.44 (0.90)
3.28 (0.91)
2.88 (0.99)
2.84 (1.01)

TABLE I: Manipulation check means and standard deviations (parentheses): voice only, feminine robot (unvoiced),masculine robot (unvoiced), stereotypically feminine task
(unvoiced), and stereotypically masculine task (unvoiced).
We evaluated whether our robot appearance and task
manipulations were successful using the unvoiced data.
Kruskal-Wallis rank sum tests showed that the feminine robot
was rated as more “feminine” than “masculine” (χ2 (1) =122.54, p < .001) and the masculine robot was rated as more“masculine” than “feminine” (χ2 (1) = 62.61, p < .001). For
the occupation, we find that the children tutor occupation
was rated as equally “feminine” and “masculine” (χ2 (1) =2.12, p = .15), while the security guard occupation was rated
as more “feminine” (χ2 (1) = 6.21, p = .012). In short, only
the appearance manipulation worked.
To answer RQ1, we used Kruskal-Wallis rank sum tests
to test whether gender perceptions were affected by voicing(see Figure 3). For the “Agender” ratings, we found no effectof voicing (χ2 (1) = 2.79, p = .095); for the “Ambiguous”
ratings, we found no effect of voicing (χ2 (1) = 0.22, p =.639); for the “Feminine” ratings, we found a main effectof voicing (χ2 (1) = 115.65, p < .001), with higher ratings
in the unvoiced (M = 3.07, sd = 1.05) compared to thevoiced condition (M = 2.32, sd = 0.97); finally, for the“Masculine” ratings, we found a main effect of voicing(χ2 (1) = 154.87, p < .001), with higher ratings in the voiced(M = 3.68, sd = 0.91) compared to the unvoiced condition(M = 2.86, sd = 1.00). Thus, we find support for H1.
To answer RQ2, we conducted a series of Kruskal-Wallis
rank sum tests to see whether gender perception was affectedby having gender-conforming robot appearance and task.
For the “Agender” ratings, we find no effect of conformity,neither for the voiced (χ2 (1) = 0.82, p = .366) nor unvoiced(χ2 (1) = 1.58, p = .209) stimuli. For the “Ambiguous”
ratings, we find no effect of conformity, neither for thevoiced (χ2 (1) = 0.03, p = .863) nor unvoiced (χ2 (1) =1.26, p = .261) stimuli. For the “Feminine” ratings, we alsofind no effect of conformity, neither for the voiced (χ2 (1) =0.36, p = .551) nor unvoiced (χ2 (1) = 0.25, p = .614)
stimuli. Finally, for the “Masculine” ratings, we also findno effect of conformity, neither for the voiced (χ2 (1) =0.140, p = .708) nor unvoiced (χ(1) = 0.04, p = .844)
stimuli. Thus, we reject H2a and H2b.
For RQ3, we used linear models on participants’ ratings ofthe voice alone to explain how they rated the voiced video
stimuli (see Figure 4). For the “Agender” ratings, we finda significant influence of the voice only ratings (b = 0.27,
95% CI [0.15, 0.38], t(109) = 4.72, p < .001); we also finda significant effect for the “Ambiguous” (b = 2.39, 95% CI[2.05, 2.73], t(109) = 13.89, p < .001) and “Masculine”(b = 0.14, 95% CI [0.04, 0.24], t(109) = 2.67, p = .009)
ratings; however, we find no effect for the “Feminine” ratings(b = 0.00, 95% CI [−0.11, 0.11], t(109) = 0.00, p = .999).
Thus, we find partial support for H3.
Finally, to answer RQ4, we conducted a series of KruskalWallis rank sum tests to see whether having genderconforming or gender-non-conforming robots influenced ratings of likeability, trustworthiness, uncanniness, creepiness,scariness, and eeriness of the robot. We do not find any
significant effect (χ2 (1) = 0.06, p = .809; χ2 (1) =0.09, p = .767; χ2 (1) = 0.32, p = .572; χ2 (1) = 0.31, p =.581; χ2 (1) = 0.39, p = .534, χ2 (1) = 0.20, p = .656,
respectively). Thus, we do not find support for H4.
V. D ISCUSSION
A gender-ambiguous voice can help reduce gendering
towards a robot that is endowed with stereotypically feminine
or masculine attributes (RQ1). People rated the robot as
more “feminine” when there was no voice compared to when

Fig. 4: Correlations between gender ratings of the voice alone
and of the voiced videos. Jitter added to aid visualisation.

the robot was combined with a gender-ambiguous voice.
As there tend to be more negative stereotypes and attitudes
towards feminine robots than masculine ones [34], [35], thisis an important result for the HRI community, suggestingthat these stereotypes could potentially be reduced with theuse of a gender-ambiguous voice.
The results from the other gender scales give additional
insights. Adding a gender-ambiguous voice to the robot did
not change participants’ ratings of it being “ambiguous” or“agender”. If people perceived the robot’s appearance as“ambiguous” or “agender”, this perception happened whether
an ambiguous voice was present or not. Thus, a genderambiguous voice does not seem to drastically change theperception of a robot that is already seen as such.
Still, the gender-ambiguous voice increased the “masculine” ratings of the robot. This is surprising given that thevoice by itself was perceived as slightly “feminine.” Given
the prevalence of a “male robot” bias [36], [37], robots
perceived as “masculine” may have been more strongly
categorised as such, with no room for ambiguity or fluidity.
This is consistent with categorical perception theories, where
the perceived difference between a stimuli and the prototype
of its category will be smaller than the actual difference [38].
In our case, the voice may not have been disruptive enoughto force the stimuli to the border between perceived genders,
in which case a stronger Uncanny Valley effect would havebeen expected [39]. If so, then the addition of an ambiguous
voice simply reinforced the existing conceptual masculine
paradigm, providing multi-modal information that lead toeven stronger categorisation than uni-modal information (thevideo without voice) [40]. This interpretation is supportedby the fact that we did not find any evidence of an Uncanny
Valley or related phenomena (RQ4), suggesting that people
never perceived the stimuli as incongruent or “uncomfortable.” This may be due to the fact that the robot occupation
manipulation was not successful. Or, in the presence ofmismatching cues (a robot perceived as “masculine” andan ambiguous or feminine-leaning voice), people integrated
these two conflicting sources of information and perceived
the stimulus as something entirely different—a more “masculine” robot—akin to the “McGurk effect” [41].
We also did not find evidence that robots conformingwith human gender stereotypes were gendered more than“mismatched” robots (RQ2). Again, this may be due tothe robot occupation manipulation not being successful.
Still, Rea et al. [42] also did not find strong evidence foroccupation-related gender stereotypes in robots. Perhaps therobot simply stating its occupation was not enough for people
to form a gender-occupation connection. On the one hand,
this is a positive result, suggesting that occupations until
recently perceived as gendered [4], [5] are not so anymore,at least for the sample of participants we selected. Still, as
gender stereotypes are still prevalent in society [20], [43], wemust extend this line of work and conduct additional pilot
testing to find occupations, or other embodiment factors, thatare currently stereotyped and have room to be disrupted.
Finally, we found that voice gendering alone influenced
gendering of the combined robot and voice stimuli for all
gender scales apart from the “feminine” one RQ3. We mustalso consider that the voice alone was rated as slightly“feminine.” This could be a ceiling effect. However, thissuggests that we are on the right track: a voice that is moreconsistently judged to be “agender” or “ambiguous” canalter gender perceptions of robots that are otherwise, perhaps
uncontrollably, gendered by participants. We commend thework that the HRI and TTS communities are performing
towards designing and developing such voices. There is stillwork to be done, as people tend to categorise voices as either“feminine” or “masculine” and it is difficult to create a truly“neutral” voice [44], [45]. In particular, we plan to involve
the genderqueer community in the design process.
There are a few limitations that should be mentioned.
Most notably, despite our pilot study suggesting otherwise,
participants in the main study still found our chosen voice
to be more “feminine” than “agender” or “ambiguous”. Thismight be due to the context in which they heard the voice(reading the encyclopaedia entry) which might have been
perceived as more “feminine”. Nonetheless, we consider our
results to be informative and promising, and we plan tocontinue exploring this topic. Furthermore, we could not
examine the interplay between the voice, the robot andparticipants’ individual differences—most notably, their own
gender, attitudes towards robots, and propensity to genderstereotype—due to space limitations. But we plan to conduct
such analyses for future work. Also, we plan to conduct
follow-up studies in person. The larger research question that
we want to address is whether gender-ambiguous voices on
robots can reduce gender stereotypes towards robots, but also
towards people.
ACKNOWLEDGEMENTS
This work was partially funded by the Digital Futures
research centre. All figures were generated with a colour
palette based on the non-binary pride flag, created byJoel Le Forestier (https://joelleforestier.com/#pridepalettes).
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