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Individual Variation in Language Attitudes Toward Voice-AI: The Role of Listeners’ Autistic-Like Traits

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More and more, humans are engaging with voice-activated artificially intelligent (voice-AI) systems that have names (e.g., Alexa), apparent genders, and even emotional expression; they are in many ways a growing 'social' presence. But to what extent do people display sociolinguistic attitudes, developed from human-human interaction, toward these disembodied text-to-speech (TTS) voices? And how might they vary based on the cognitive traits of the individual user? The current study addresses these questions, testing native English speakers' judgments for 6 traits (intelligent, likeable, attractive, professional, human-like, and age) for a naturally-produced female human voice and the US-English default Amazon Alexa voice. Following exposure to the voices, participants completed these ratings for each speaker, as well as the Autism Quotient (AQ) survey, to assess individual differences in cognitive processing style. Results show differences in individuals' ratings of the likeability and human-likeness of the human and AI talkers based on AQ score. Results suggest that humans transfer social assessment of human voices to voice-AI, but that the way they do so is mediated by their own cognitive characteristics.
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Individual variation in language attitudes toward voice-AI:
The role of listeners’ autistic-like traits
Michelle Cohn1, Melina Sarian1, Kristin Predeck1, Georgia Zellou1
1Phonetics Laboratory, Department of Linguistics, UC Davis, USA
Abstract
More and more, humans are engaging with voice-activated
artificially intelligent (voice-AI) systems that have names
(e.g., Alexa), apparent genders, and even emotional
expression; they are in many ways a growing ‘social’
presence. But to what extent do people display sociolinguistic
attitudes, developed from human-human interaction, toward
these disembodied text-to-speech (TTS) voices? And how
might they vary based on the cognitive traits of the individual
user? The current study addresses these questions, testing
native English speakers’ judgments for 6 traits (intelligent,
likeable, attractive, professional, human-like, and age) for a
naturally-produced female human voice and the US-English
default Amazon Alexa voice. Following exposure to the
voices, participants completed these ratings for each speaker,
as well as the Autism Quotient (AQ) survey, to assess
individual differences in cognitive processing style. Results
show differences in individuals’ ratings of the likeability and
human-likeness of the human and AI talkers based on AQ
score. Results suggest that humans transfer social assessment
of human voices to voice-AI, but that the way they do so is
mediated by their own cognitive characteristics.
Index Terms: language attitudes, voice-activated artificially
intelligent (voice-AI) systems, sociolinguistic competence
1. Introduction
Prior work has shown that people have strong beliefs and
opinions of other individuals based solely on speech patterns
[1]. These language attitudes have been linked to associations
between speech variation and geographic region [2], [3],
gender [4], social class [5], and native language [6]. Language
attitudes are often a proxy for people’s social attitudes toward
individuals from a particular regional or social group.
Specifically, people attribute a person who speaks with an
accent as being inherently ‘pleasant’ and ‘smart’, or inversely
‘harsh’ and ‘not intelligent’, if they associate people from the
social group the accent indexes as having that attribute [7],
[8]. In other words, people have intricate folk beliefs about
inherent cognitive and social characteristics of speakers based
purely on their voice and speech patterns.
An open question is whether people attribute similar
language attitudes to humans and to voice-activated artificially
intelligent (voice-AI) systems (e.g., Apple’s Siri, Google
Assistant, and Amazon’s Alexa). Given the ubiquity of these
systems [9], there is growing interest in the implications of
social characteristics of voice-AI, such as gender stereotyping
of predominantly female assistants [10]–[12]. But our
scientific understanding of how people attribute social
variables to voice-AI is still limited.
The current study examines human and voice-AI language
attitudes through the lens of the Computers as Social Actors
(CASA) theoretical framework [13], [14]. CASA posits that
people automatically apply social behaviors from human-
human interaction to their behaviors toward computers given
that a cue of humanity is expressed by the system. In
particular, the use of spoken language has been proposed to
‘activate’ human social norms toward technology [15]. For
example, [16] found that participants perceived differences in
personality across synthesized text-to-speech (TTS) voices
(e.g., labeled some as being ‘introverted’ or ‘extroverted’)
based on the acoustic parameters of the voices. Relatedly, [17]
found that a robot with a higher pitched voice was given
higher ratings in overall appearance, voice appeal, behavior,
and personality, relative to with a lower pitched voice. For
modern voice-AI, TTS synthesis is based on datasets of
productions from real human speakers, via concatenative TTS
[18] or neural vocoders trained on human pronunciation
patterns (e.g., Tacotron or Wavenet [19], [20]). Given their
more ‘human-like’ voices, a CASA account might predict that
listeners will ascribe social judgments to voice-AI in the same
ways that they would toward a real human, even if they know
that they are interacting with a non-human entity.
Related work in human-robot/computer interaction
provides some support for the possibility that humans will rate
TTS voices using human-based attributes. For example,
individuals’ attitudes of a robot have been shown to vary
based on the dialect it ‘speaks’ (e.g., Acapela TTS child
voices presented via NAO robots in [21]; US, UK, NZ dialect
ratings for a health robot in [22]). Similarly, listeners assign
age and gender to TTS voices presented (e.g., IBM TTS in
[23]), suggesting that social attribution of a voice does not rely
on the presence of a physical form. Even fewer studies have
made a direct comparison between both TTS and naturally-
produced human voices. Such a comparison is essential for
evaluating predictions made by the CASA framework: people
might apply social rules from human-human interaction to
interactions with technology, but it is possible that there is still
a distinction between real human versus voice-AI. For
example, [24] found that a human voice was rated as nicer,
less eerie, less supernatural, less hair-raising, and less
shocking than a TTS voice. [25] found that a human voice was
rated as having stronger social presence and behavioral
intentions, while a TTS voice received lower trust ratings.
1.1. Individual variation in language attitudes
An additional consideration is whether computer
personification responses (here, application of language
attitudes to voice-AI) might vary across individuals.
Individual variation in behavioral responses to speech is well
attested: there are differences across people within the same
age, social group, and region in how speech patterns are
perceived [26]. In particular, variation across listeners has
{mdcohn, msarian, kpredeck, gzellou} @ucdavis.edu
Copyright © 2020 ISCA
INTERSPEECH 2020
October 25–29, 2020, Shanghai, China
http://dx.doi.org/10.21437/Interspeech.2020-13391813
been linked to differences in individuals’ cognitive processing
style (see [27] for review); that is, how people cognitively
vary in how they process sensory information [28].
There is increasing evidence that there is individual
variation in personification of technology, as well. For
example, in a self-reported diary study of human-Alexa
interactions, [29] found that only half of participants reported
human social behaviors toward Alexa (e.g., saying “please”
and “thank you”). This suggests that the application of
politeness norms from human-human interaction to
interactions with technology (cf. [30]) varies across users. For
example, [31] found that the extent to which participants
responded positively to a computer’s flattering praise varied
as a function of their cognitive style: individuals with less
analytical and more intuition-driven traits were more greatly
affected by the computer’s flattery. Also, [32] found that
participants’ subconscious vocal entrainment behavior toward
human and Siri voices varied based on their cognitive
processing styles, measured by the Autism Quotient (AQ).
The AQ [33] is a common non-clinical instrument across
studies of speech and language behavior used to assess
differences in individuals’ cognitive processing style [26],
[32]. The AQ has been shown to capture variation within
neurotypical populations and is consistent with those formally
diagnosed with Autism Spectrum Disorder (ASD), a condition
that results in significant atypicality in social, emotional, and
communicative behavior (DSM-5 [34]). In a general
population of people, without a clinical ASD diagnosis,
autistic-like traits manifest to varying degrees and can be
quantified [35]. The AQ has also been shown to capture
differences in behavior across individuals. For instance,
people with higher AQ scores, signaling more autistic-like
traits, were less accurate in detecting whether a robot’s actions
were pre-programmed or human-controlled [36].
Given that one of the primary characteristics for
individuals with more autistic-like traits is a deficit in emotion
perception [37], [38], presenting participants with emotionally
expressive stimuli is one way to further probe the social nature
of these interactions and emphasize possible sources of more
subtle individual variation. This has been previously
demonstrated for emotion expression in robots: individuals
with more autistic-like traits display weaker sensitivity to a
robot’s fear and disgust facial expressions [39], [40]. For
voice-AI, emotional expressiveness can be conveyed in some
systems. For instance, the Amazon Alexa (US-English) voice
has ‘Speechcons’ [41]: words and phrases that have been
recorded in a hyper-expressive manner by the voice actor.
Prior work suggests that participants respond positively to
these emotionally expressive productions by voice-AI,
displaying more vocal entrainment toward emotionally
expressive interjections by Alexa [42] and rating interactions
with an Alexa social bot more highly if it contained them [43].
We predict that differences based on AQ will manifest in
different social judgments of Alexa and human voices who
use emotionally expressive speech. Therefore, in the present
study, we exposed participants to these same emotionally
expressive interjections to further increase possible variation
in responses based on AQ.
1.2. Current Study
We designed the current study to explore differences in
people’s social judgments of voice-AI talkers, compared to a
human’s utterances. Participants completed a short interactive
task, where they heard neutral and hyper-expressive
interjections produced by the default Amazon Alexa voice and
a real human female speaker. Then, participants rated the
speakers across 6 social traits: how intelligent, professional,
likeable, attractive, human-like, and old each voice sounded.
We selected these ratings based on related work in human-
human (e.g., ‘intelligence’ in [7]; ‘professional’ in [44];
‘likeable’ in [45]; ‘attractive’ in [46]; ‘age’ in [47]), and
human-computer interaction (‘human-like’ and ‘age’ in [23]).
Participants also completed the Autism Quotient (AQ) [33].
We then tested the extent the relationship between AQ scores
and ratings for the voice-AI and the human talkers.
We have several predictions about the relationship
between an individual’s autistic-like traits and their social
ratings of a voice-AI and a naturally produced human voice.
First, we predict that people who display more autistic-like
traits will show more variation in their social ratings in
general (i.e., for humans and voice-AI); this is in line with
prior work showing that individuals with ASD display
difficulty with social evaluation [48].
Second, we predict that individuals with higher AQ scores
will attribute more positive social judgments to the voice-AI
talker (relative to the human talker). This prediction stems
from work showing that individuals with ASD often prefer
interactions with technology over those with humans [49],
[50]. One way to understand this relationship is through the
lens of the Uncanny Valley of the Mind [51], a function
proposed to capture the dynamics of increasing human-
likeness on likeability: while usually increasing humanness
correlates with increasing likeability, at a point nearing the
‘human’ boundary, humans respond with discomfort or
disgust. This ‘valley’, however, has been proposed to be
shifted in individuals with ASD [52], occurring with the most
‘human-like’ humans. Put another way, increasing autistic-
like traits may led to greater uncanniness of the human,
relative to the voice-AI, talker. In the present study, this could
be realized as lower ratings for the human voice.
2. Methods
2.1. Stimuli, Participants, and Procedure
Stimuli for the interactive task consisted of 24 interjections,
used in [42], generated in neutral and emotionally-expressive
prosodies: awesome, bravo, bummer, cheers, cool, darn, ditto,
dynamite, eureka, great, howdy, hurray, jinx, roger, shucks,
splash, super, wow, wowzer, yikes, yuck, yum, zap, zing. Items
were selected from the Alexa ‘Speechcons’ available [41].
The neutral Alexa productions were generated using the Alexa
Skills Kit (ASK). For the human voice, a female native
English speaker (age 25) was recorded producing the same set
of interjections. These productions were elicited using
instructions to speak in an emotionally neutral or expressive
manner; the speaker did not imitate the Alexa voice. The
recordings were made in a sound attenuated booth while the
speaker wore a head-mounted microphone (Shure WH20
XLR) at a 44.1 kHz sampling rate. All stimuli were amplitude
normalized to 65 dB in Praat. (Though normalizing might
reduce acoustic cues to expressiveness, we did so to maintain
perceptual loudness across items).
Participants (n=34), native English speakers recruited
from the UC Davis Psychology subjects pool (21 M, 13 F;
mean age = 20.12 years, sd = 2.2) were first familiarized with
the words by reading them aloud. Then, participants were
exposed to both speakers’ productions. In this phase,
participants were first introduced to each interlocutor, one at a
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time; either the voice-AI system (‘Alexa’) or human
(‘Melissa’) first (Speaker order blocked and counterbalanced
across participants). On a trial, participants heard an item
produced by a talker and were asked to repeat the word. Both
neutral and emotionally expressive productions were
randomly presented within each block (2 blocks per speaker).
After the exposure phase, participants rated each talker’s
voice for 6 traits using a sliding scale (ranging from 0-100)
(see Table 1). Order of speaker block (human, voice-AI) was
counterbalanced across participants. Following the ratings
task, participants completed the AQ questionnaire.
Table 1: Social Attribute Ratings
Professional
How professional did ___ sound?
(0=not professional, 100 =extremely professional)
Likeable
How likeable did ___ sound?
(0=not likeable, 100 =extremely likeable)
Attractive
How attractive did ___ sound?
(0=not attractive, 100 =extremely attractive)
Intelligent
How intelligent did ___ sound?
(0=not intelligent, 100 =extremely intelligent)
Human-like
How much like a real person did ___ sound?
(0=not like a real person, 100=extremely realistic)
Age
How old did ___ sound? (0-100 in years)
2.2. Autism Quotient
The AQ questionnaire [33] consists of 50 statements designed
to quantify the extent of autistic-like traits in adults of normal
intelligence in a non-clinical setting. There are 5 categories of
questions, assessing cognitive dimensions specifically
associated with ASD: social skills, attention switching,
attention to detail, communication, and imagination. For each
statement, participants pick one of four answers “definitely
agree”, “slightly agree”, “slightly disagree”, and “definitely
disagree”. We followed the binary coding of responses as 1 or
0, with 1 corresponding to a more autistic-like response; 0
corresponding to a less autistic-like response. The total score
is summed such that a higher value indicates more autistic-like
traits, ranging from 0 (no autistic-like) to 50 (highly autistic-
like).
3. Analysis & Results
3.1. AQ Scores
We observed variation in participants’ overall AQ scores
(range=8-31, mean = 17.7, sd = 5.7). The standard deviation
of all social ratings (collapsed across the six variables) was
modeled with a linear regression with a fixed effect of AQ
score (continuous 0-50) with the lme4 R package [53]. The
model did not reveal an effect of AQ score on overall ratings
variation [β=-0.01, t=-0.03, p=0.97]. Overall, the intercept for
all ratings was 60.2.
3.2. Social Ratings Models
We modeled each social rating as a continuous dependent
variable (0-100) with separate linear mixed effects models.
Each model contained identical fixed and random effects
structure. Fixed effects included Talker (2 levels: Alexa,
human; contrasts were sum coded), AQ score (continuous: 0-
50), and their interaction. Random effects included by-Subject
random intercepts. (Models with the added by-Subject random
slope for Talker did not converge; note that a separate model,
with AQ as a 4-point scale, did not improve model fit).
3.2.1. Attractiveness, intelligence, professionalness
The attractiveness, intelligence, and professionalness ratings
models all showed no significant effects or interactions. The
intercepts were all above 50 (51.9 attractive, 64.9 intelligent,
70.5 professional), indicating that participants rated both
voices as similarly attractive, intelligent, and professional.
3.2.2. Likeability, age, human-likeness
The likeability ratings model computed a significant main
effect of Talker, where participants judged the Alexa speaker
to more likeable, overall, than the human speaker [β=-15.2,
t=-2.7, p<0.05]. This effect was additionally modulated by an
interaction between Talker and AQ score [β=-0.99, t=-3.2,
p<0.01]. This interaction is depicted in Figure 1.A; as
participants’ AQ scores increase, they are more likely to
report distinct likeability ratings for the voice-AI and for the
human talker: higher for the real person and lower for Alexa.
For participants’ estimates of the speakers’ ages, the model
computed a main effect of Talker: the Alexa voice was rated
as sounding older than the human voice [β=6.5, t=2.6,
p<0.05]. Figure 1.B displays this main effect. There were no
other significant effects or interactions.
The human-likeness ratings model revealed a significant
interaction between Talker and AQ score [β=-1.2 t=-3.2,
p<0.01]. This interaction can be seen in Figure 1.C: as
participants’ AQ scores increase, they are more likely to rate
the human as more human-like and the Alexa talker as less
human-like. Participants with lower AQ scores (indicating less
autistic-like traits) rate the Alexa speaker and the human
speaker as more similar in their human-likeness. No other
effects were observed.
4. Discussion
In this study, we examined whether participants attribute
language attitudes to a voice-AI interlocutor (here, Amazon’s
Alexa) and a real human talker in similar ways, and whether
there are patterns of individual variation in these ratings.
Overall, we found no difference in the ratings for the Alexa
and human voice with respect to three dimensions: how
intelligent, professional, and attractive they sound. Still,
listeners did hear other differences in the voices: they rated the
Alexa voice as more likeable and slightly older than the
human voice. These patterns show that listeners extract subtle
personality and age-related cues for TTS voices. Here, Alexa
was rated as being in her 30s, while the human voice was
rated as being in her 20s. Together, these overall ratings
patterns provide support for the CASA personification
framework [13], [14]: humans are applying social labels to
voice-AI that are, in some cases, similar across the
interlocutors (intelligence, professional, attractive).
In addition to these general patterns, we also tested whether
there was variation in ratings based on an individual’s autistic-
like traits. While our first prediction was that individuals with
more autistic-like traits would show greater variation in scores
in general, we did not find evidence for this: there was no
relationship between increasing AQ score and overall
variation in ratings. This is contra what was observed in [48],
where they saw differences in social evaluations in individuals
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with ASD. As our participant pool consisted of individuals
without a formal ASD diagnosis – in assessing individual
differences in the general undergraduate population – this
could be one explanation as to why differences in our study
were minimal.
At the same time, we did observe patterns of variation
with some of the ratings: individuals with more autistic-like
traits were more likely to provide distinct ratings for the Alexa
and human voices. Contrary to our prediction, however, these
differences were not in the expected direction; rather, higher
AQ score was associated with a decrease in likeability and
human-likeness of the Alexa voice and an increase in both
dimensions for the human voice. One way to interpret this
finding is that individuals with more autistic-like traits
categorize human and voice-AI interlocutors as distinct social
categories, and subsequently rate them more distinctly. These
findings can add to prior work outlining Uncanny Valley [51]:
here, we see that a voice-AI interlocutor who produces speech
with neutral and expressive emotion is less likeable and less
human-like for individuals with greater autistic-like traits.
While we did not see an uncanny ‘cliff’ for autistic-like traits,
as proposed for ASD [52], this is not to say that such cliff
does not exist; rather, our findings suggest that the poles of
human-likeness (machine ß à human) may be more distinct
for individuals with greater autistic-like traits.
While this work provides evidence that humans apply
language attitudes toward voice-AI, there are many questions
that remain. For one, how these attitudes may interact with
gender is an open question. In this study, we held gender
constant, only including female voices, given the availability
of ‘Speechcons’ [41] for the Alexa female voice. Yet,
expanding to other genders – and comparing multiple voices –
will be critical next steps in this line of research. Furthermore,
gender of the participants may also be a relevant factor in
these social evaluations. Additionally, a person’s experience
with voice-AI may be another factor in whether, and to what
degree, they might apply language attitudes toward voice-AI
in similar ways as they do for human voices.
Finally, another open avenue for future work is whether
the top-down label of voice-AI and human may lead to
different social ratings. In the current study, these labels
always matched (i.e., TTS acoustic productions paired with
knowledge that the speaker was a voice-AI system). Future
work could test how listeners, varying in AQ, differently
weigh these factors (different voice quality or different
speaker category).
5. Acknowledgments
This material is based upon work supported by the National
Science Foundation SBE Postdoctoral Research Fellowship
under Grant No. 1911855 to MC. This work was partially
supported by an Amazon Faculty Research Award to GZ.
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... This feature serves both convenience and accessibility, allowing users to enter text without manually typing. Dictation tends to involve relatively slow speech, typically that of a single speaker, who is aware they are interacting with a device, and who may consciously modify their speech patterns to facilitate device understanding (Cohn et al., 2020). Dictation may have applications in many fields. ...
... This approach serves to complement studies done with users talking to devices in their home (e.g., Mallidi et al., 2018;Huang et al., 2019) and also pinpoint differences that might be present due to other factors in the situation (e.g., physical distance from the microphone; rate and type of automatic speech recognition (ASR) errors). While TTS methods have advanced in recent years (e.g., Wavenet in Van Den Oord et al., 2016), our participants rated the two talkers as distinct in their human-likeness: Alexa was less human-like than the human voice, consistent with prior work (Cohn et al., 2020b;. ...
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