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Differences in Gradient Emotion Perception: Human vs. Alexa Voices

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The present study compares how individuals perceive gradient acoustic realizations of emotion produced by a human voice versus an Amazon Alexa text-to-speech (TTS) voice. We manipulated semantically neutral sentences spoken by both talkers with identical emotional synthesis methods, using three levels of increasing 'happiness' (0 %, 33 %, 66 % 'happier'). On each trial, listeners (native speakers of American English, n=99) rated a given sentence on two scales to assess dimensions of emotion: valence (negative-positive) and arousal (calm-excited). Participants also rated the Alexa voice on several parameters to assess anthropomorphism (e.g., naturalness, human-likeness, etc.). Results showed that the emotion manipulations led to increases in perceived positive valence and excitement. Yet, the effect differed by interlocutor: increasing 'happiness' manipulations led to larger changes for the human voice than the Alexa voice. Additionally, we observed individual differences in perceived valence/arousal based on participants' an-thropomorphism scores. Overall, this line of research can speak to theories of computer personification and elucidate our changing relationship with voice-AI technology.
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Differences in Gradient Emotion Perception: Human vs. Alexa Voices
Michelle Cohn1, Eran Raveh2, Kristin Predeck1, Iona Gessinger2, Bernd Möbius2, Georgia Zellou1
1Phonetics Laboratory, Linguistics, UC Davis, Davis, California, USA
2Language Science and Technology, Saarland University, Saarbrücken, Germany
{mdcohn, kpredeck, gzellou}@ucdavis.edu, {raveh, gessinger, moebius}@coli.uni-saarland.de
Abstract
The present study compares how individuals perceive gradient
acoustic realizations of emotion produced by a human voice
versus an Amazon Alexa text-to-speech (TTS) voice. We ma-
nipulated semantically neutral sentences spoken by both talk-
ers with identical emotional synthesis methods, using three
levels of increasing ‘happiness’ (0 %, 33 %, 66 % ‘happier’).
On each trial, listeners (native speakers of American English,
n=99) rated a given sentence on two scales to assess dimen-
sions of emotion: valence (negative-positive) and arousal (calm-
excited). Participants also rated the Alexa voice on several pa-
rameters to assess anthropomorphism (e.g., naturalness, human-
likeness, etc.). Results showed that the emotion manipulations
led to increases in perceived positive valence and excitement.
Yet, the effect differed by interlocutor: increasing ‘happiness’
manipulations led to larger changes for the human voice than
the Alexa voice. Additionally, we observed individual differ-
ences in perceived valence/arousal based on participants’ an-
thropomorphism scores. Overall, this line of research can speak
to theories of computer personification and elucidate our chang-
ing relationship with voice-AI technology.
Index Terms: voice-activated personal assistants, emotion per-
ception, human-computer interaction
1. Introduction
While the primary function of speech is to communicate a mes-
sage to our interlocutor, the voice also carries other properties,
including social details (e.g., region, age, gender) and even our
emotional state. Whether we are happy, surprised, sad, or an-
gry might be conveyed on an utterance [1]. Emotional expres-
siveness has been targeted as a way to make synthetic, text-
to-speech (TTS) voices more engaging to human users [2, 3].
These efforts have concentrated primarily on synthesizing ba-
sic human emotions, including happiness, sadness, anger, fear,
disgust, and surprise [4]; yet perception of such qualities in the
synthesis, however, is not always clear to the listeners [4]. The
parameters adjusted in emotional synthesis may be contribut-
ing to this confusion. Another contributor may be the quality
of the synthetic voices; it may be the case that it is difficult for
listeners to extract emotion from more robotic-sounding voices.
Finally, a third contributor may be the degree to which listen-
ers attribute human-like emotion to the systems – which may
be due to the human-like characteristics of the system as well
as individual differences in personification of the systems. The
present paper examines these factors in how listeners perceive
emotion in a real human and an Amazon Alexa TTS voice.
1.1. Emotion and modern voice-AI systems
In the last decade, modern voice-activated, artificially intelli-
gent (voice-AI) systems, such as Apple’s Siri, Amazon’s Alexa,
and Google Assistant, have become a common household inter-
locutor for many human users, particularly in the United States
[5]. These systems engage users in a variety of functional and
social tasks. For example, users may ask Alexa to “turn off the
light”, “tell a joke”, or even have a conversation [6]. Prior work
suggests that humans apply social knowledge to their speech in-
teractions with voice-AI systems, such as gender-related asym-
metries [7]. There is some initial evidence that users may also
be perceptive to emotional expressiveness in voice-AI systems:
speakers vocally align to emotionally expressive productions
by the Alexa voice [8] and rate conversations with an Amazon
Alexa socialbot higher when the bot uses emotionally expres-
sive interjections [9], but neither of these studies employed a di-
rect human comparison. The present study addresses this gap,
examining whether listeners similarly perceive gradient emo-
tion conveyed in TTS and natural human productions.
1.2. Emotion and CASA
Comparing responses to emotion produced by human and
voice-AI interlocutors can speak to computer personification
theories, such as the Computers Are Social Actors (CASA) theo-
retical framework [10, 11], which holds that humans treat tech-
nology as a social actor in interactions and apply social rules
and norms from human-human interaction (HHI). This personi-
fication of technology is thought to be automatic, subconscious,
and driven by the fact that device interaction often involves sim-
ilar aspects as HHI. For example, participants assigned higher
trustworthiness and likeability ratings to a computer system
that displayed more empathetic emotion than one that did not
[12]. In another study, participants showed different negotia-
tion strategies when haggling with a ‘happy’ or an ‘angry’ com-
puter system, in line with emotion-based asymmetries observed
in human-human negotiation [13]. Meanwhile, other work has
found that negative reactions are triggered by computer behav-
ior in the same ways that a human’s actions might engender
anger: after a computer system had acted unfairly in a bargain-
ing game, participants in that interaction displayed anger and
spiteful behavior toward the device [14].
In line with CASA [11], there is some evidence for similar
perception of emotion produced by a human or computer. In
a study examining explicit emotion identification (e.g., happy,
sad, surprised, etc.) based on visual and prosodic differences,
participants displayed equal responses to the ‘human’ or ‘com-
puter’ guise [15]. In a study of facial expression, Noël et al.
[16] found that subjects’ accuracy identifying emotion for a
real human face and a digital avatar was equal when context
and emotional expressiveness were congruent. Following these
studies, one possibility is that individuals will interpret emo-
tional prosody similarly for human and voice-AI speech.
On the other hand, many studies exploring synthesized
emotion do not make a direct human versus device comparison.
It is possible that while there are similarities in the gross pat-
Copyright © 2020 ISCA
INTERSPEECH 2020
October 25–29, 2020, Shanghai, China
http://dx.doi.org/10.21437/Interspeech.2020-19381818
terns of social responses toward humans and computers/robots,
there may be more fine-grained nuances that are missed, par-
ticularly in using a between-subjects design [e.g., 8, 9, 12, 13,
14, 17]. For example, participants rated the emotion of syn-
thetic and natural speech similarly when the emotional expres-
sion was congruent with the content (e.g., happy prosody with
positive content) [17]. Yet, in the incongruent conditions (e.g.,
sad prosody with positive content), they observed differences
for the human and TTS voices in the relative weight listeners
gave to the prosody, relative to the content: they rated synthetic
speech as ‘happier’ than human speech when it was produced
with sad prosody and happy content. Indeed, there is some evi-
dence that humans respond to voice-AI and human speech dif-
ferently: participants display less vocal alignment toward Siri
and Alexa TTS voices than human voices [18, 19]; this suggests
that voice-AI systems may be a distinct type of social actor than
another human. Therefore, in the present study, one prediction
is that participants may show weaker emotion perception for an
Alexa voice, relative to a human voice.
Additionally, many TTS emotion perception studies ask
participants to classify very distinct types of stimuli (e.g., ba-
sic emotions of happiness or sadness); one unexplored question,
to our knowledge, is whether the perceived magnitude of emo-
tional expression is similar for human and synthetic voices. In
the current study, we examine gradience in emotion perception
by adapting neutral speech produced by a human and voice-
AI talker (here, Amazon Alexa) at three happiness levels (see
Section 2.1.2). One possibility is that listeners will be more
sensitive to gradient emotional display by human voices, as hu-
man voices are more socially meaningful. Alternatively, an-
other possibility is that listeners will display equal sensitivity
for human and voice-AI voices producing multiple levels of an
emotion, which would provide support for the CASA account.
1.3. Variation in personification
While the CASA account proposes an automatic mechanism of
personification, there is reason to believe that any such response
will vary considerably across individuals. For example, partic-
ipants displayed different patterns of vocal alignment toward
voice-AI (Apple Siri) voices based on their cognitive process-
ing style [20]. In another study, individuals interacting with the
same robot receptionist communicated differently depending on
their attitude towards the virtual interlocutor: as being more
‘human-social’ or a ‘computational-tool’ [21]. In the present
study, we assess each participant’s anthropomorphism of the
virtual assistant Alexa across several dimensions, viz. human-
ness, naturalness, etc., in a pre-experiment survey. We predict
that overall anthropomorphism scores will be related to voice-
AI emotion perception, i.e., individuals with higher anthropo-
morphism scores are expected to be more perceptive to emotion
by the Alexa voice.
1.4. Current study
In the present study, we examine whether technology personifi-
cation is gradiently realized in the perception of emotion. In this
experiment, we ask two principal questions: 1) Do listeners per-
ceive acoustic variations conveying different levels of emotional
state similarly for human and TTS voices?, and 2) Does an indi-
vidual’s gradient perception of TTS voice emotion vary accord-
ing to the degree to which they personify the system; that is,
are listeners better at perceiving emotion for interlocutors they
deem as being more ‘human-like’? While the general acous-
tic properties of a recorded human voice and an Alexa voice
differ, we used identical parameters for both voices in the emo-
tional synthesis system, DAVID [22]. We selected DAVID given
its prior validation: listeners perceive the intended emotions
(e.g., happiness, sadness, and fear) in manipulated productions
[22]. Additionally, DAVID allows for specification of gradient
change toward a given emotion (e.g., 66% ‘happier’).
Critically, we test whether the same gradient manipulations
of emotional prosody within a given voice yields similar or dif-
ferent changes in emotion perception across the two speakers
(here, human vs. voice-AI). As our aim is to investigate the role
of emotional prosody, we conducted a norming study (see Sec-
tion 2.1.1) of sentences to generate our list of ‘emotionally neu-
tral’ sentences; accordingly, listeners would primarily respond
to the emotional properties conveyed through the voice.
2. Methods
2.1. Stimuli
2.1.1. Norming study: emotionally neutral sentences
We selected sentences that had previously been rated as emo-
tionally ‘neutral’ (14 from Russ et al. [23]; 10 from Ben-David
et al. [24]; and 2 from Mustafa et al. [25]) as well as 94 declar-
ative sentences from the Speech Perception In Noise (SPIN) test
[26], to a total of 120, for an online emotional valence norming
study. The inclusion of the SPIN sentences permits a greater
range of perceived valence. The 48 native English speakers
(mean age 19.7 ±2.1 years; recruited through the UC Davis
subject pool) rated the emotion in all 120 sentences, which were
randomly presented on the screen one at a time with no sound.
On a given trial, they saw a sentence and used a sliding scale to
indicate how negative, positive, or neutral it was; the beginning,
middle, and end of the spectrum were labeled with “0 = nega-
tive”, “50 = neutral”, and “100 = positive”, respectively. The
slider position reset to 50 at the beginning of each trial. The
data are available as supplemental material1.
2.1.2. Synthesizing emotion in human and Alexa voices
We selected the 15 sentences with the ratings closest to 50
(range 48 to 51, mean 49.9) from the norming study (Sec-
tion 2.1.1), excluding imperatives and sentences with personal
pronouns (e.g., “My T.V. has a twelve-inch screen.") that may
be incongruous if produced by a voice-AI system. We also ex-
cluded two sentences with negative words (e.g., “garbage" and
“shipwrecked"). The remaining 15 sentences had 4 to 8 words
(mean 5.9 ±1.2). We recorded a native English female speaker
producing the 15 target sentences in citation format. We gen-
erated the same 15 sentences with default US-English female
Alexa voice using the Alexa Skills Kit. Recordings had a sam-
pling rate of 44.1 kHz and were amplitude normalized2based
on mean intensity measurements in Praat [27].
Next, we generated three ‘happiness’ levels (at 0 % (no
change), 33 %, and 66 % happier) with the DAVID emotional
synthesis platform [22] in the Max programming language [28].
We used the DAVID default values for ‘happiness’, including
a fundamental frequency (f0) increase of 30 cents3, and high
shelf filter (8 kHz, gain 3 dB). We passed all sentences through
1http://dx.doi.org/10.17632/tm2scpw8mg.1
265 dB for human, 64 dB for Alexa voices; as the Alexa samples
were generated in a systematically different manner than the human
recordings (i.e., not through air transmission), this normalization was
relative and adjusted (by ear) by the first author.
3A cent is a logarithmic unit of pitch (1 octave = 100 cents)
1819
52
53
54
55
56
0 33 66
Happiness level
Valence score
Interlocutor
Alexa
Human
Perceived valence
Perceived valence
Valenc e sc ore (0-100)
B
Happiness Level
Perceived arousal
C
34
36
38
0 33 66
Happiness level
Arousal score
Interlocutor
Alexa
Human
Perceived arousal
Happiness Level
more
positive
Arousal score (0-100)
more
excited
Alexa
Human
Alexa
Human
A
Amanda
(Human)
Alexa
Interloctuor Image
Figure 1: (A) Human and synthetic speakers’ silhouettes. The corresponding silhouette appeared on the screen for all trials within a
speaker block in the human (‘Amanda’) or device (‘Alexa’) condition. (B-C) Summary of valence (B) and arousal (C) results. The blue
dots and green triangles indicate the mean scores for Alexa and the human voices, respectively. Error bars show the standard error.
the DAVID re-synthesizer at 0 %, 33 %, and 66 % of the ‘hap-
piness’ parameters (e.g., 33 % increase in f0toward 30 cents:
increase of 9.9 cents). This resulted in a total of 90 stimuli4.
(15 sentences ×3 happiness levels ×2 interlocutors).
2.2. Participants
Participants (none of whom completed the norming task) con-
sisted of 99 native speakers of American English, recruited from
the UC Davis Psychology subject pool (70 females, 29 males;
mean age 20.2 ±2.2 years); 81 of them reported some experi-
ence using a voice-AI system.
2.3. Procedure
Subjects completed the experiment online, via Qualtrics. First,
they provided basic demographic information, as well as their
voice-AI usage. Next, participants completed an audio calibra-
tion step to ensure that the stimuli were audible and understand-
able via their computer’s audio device: they heard one sentence
(not used in the experimental trials) produced by each interlocu-
tor (human and Alexa) and were asked to select what they heard
out of a set of options; if their response was correct, they con-
tinued to the experimental trials; if not, they were taken to a
screen that indicated that they needed to increase the volume.
Participants could not continue to the experimental trials until
they answered correctly.
Then, they completed a voice-AI anthropomorphism sur-
vey, adapted from Ho and MacDorman [29]. Using sliding
scale response (0-100), participants heard a single sentence pro-
duced by the Amazon Alexa voice (note that the sentence was
not manipulated in terms of emotion) and rated to what de-
gree they thought the voice was machine-like/human-like, ar-
tificial/natural, eerie/comforting, and cold/warm.
In the experimental trials, participants were told that they
would hear sentences produced by either an Amazon Alexa or a
real person (‘Amanda’), rate the sentences, and answer a few
randomly presented listening comprehension questions. Par-
ticipants were told that they would only hear each sentence
once, and to respond as quickly and accurately as possible.
Speaker condition (voice-AI/human) was divided into blocks
(order counterbalanced across subjects). During all trials of
each block, participants saw the corresponding Alexa/human
silhouette on the screen (see Figure 1.A). On each trial, sub-
jects heard an emotionally neutral sentence in one of the three
4http://www.coli.uni-saarland.de/~raveh/Interspeech_2020/stimuli/
happiness levels and rated it on two dimensions of emotion us-
ing a sliding scale: valence (0 = negative, 50 = neutral, 100 =
positive) and arousal (0 = calm, 50 = neutral, 100 = excited). At
the beginning of each trial, the slider position reset to 50. The
sentences were only presented aurally and were randomized by
happiness level. Each participant rated all 90 stimuli. Addition-
ally, listeners heard a listening comprehension question after the
experimental trials for each speaker: they heard a semantically
anomalous sentence produced by the speaker (either human or
Alexa) and identified the sentence from a multiple choice list.
Participants needed to answer correctly to receive credit for the
study. In total, the experiment took roughly 30 minutes.
2.4. Analysis
We analyzed participants’ valence and arousal scores for the
sentences with separate linear mixed models (LMMs), using
the lme4 R package [30]. In both models, the fixed effects in-
cluded HA PPI NE SS L EVE L (3 levels: 0 %, 33 %, and 66 % hap-
pier), IN TER LO CUT OR (2 levels: human, device), and all possi-
ble interactions. Random effects included by-S UBJEC T random
intercepts, with by-SUBJECT random slopes for INTERLOCU-
TOR. The linear mixed models (sLMMs) were fit by REML
t-tests and used Satterthwaite approximations to determine the
degrees of freedom. The p-values were derived from the output
of these fits with the lmerTest package [31].
For the anthropomorphism analysis, we calculated a com-
posite anthropomorphism score, summing the totals for each
of the responses (human-like, natural, comforting, warm) for
the voice-AI; a higher score indicates greater personification.
On the subset of data for the Alexa talker, we modeled valence
and arousal scores in separate linear mixed models (LMMs).
Main effects included ANTHROPOMORPHISM SCORE (contin-
uous) and HA PP INE SS L EVE L, their interaction, as well as by-
SUBJECT random intercepts.
3. Results
Figure 1 shows the mean scores of valence and arousal for the
Alexa and human voices over the three levels of happiness. The
outcomes of the LMM fits (see Section 2.4) for valence and
arousal are summarized in Tables 1 and 2. Valence scores were
overall lower for the human speaker relative to Alexa. There
was also an interaction between HA PPINE SS LEVEL and IN-
TE RLO CU TOR: there was a larger increase in valence for the
human talker at the higher happiness levels (33 % and 66 %)
(see Figure 1.B). While the score difference between the human
1820
Table 1: Summary of fixed effects in valence scores.
Coef SE df t p
(Intercept) 55.31 1.31 108 42.06 0.001 ***
Happ.33 0.56 0.49 8708 1.14 0.250
Happ.66 0.18 0.49 8708 0.38 0.710
Int.Human 3.27 1.12 129 2.93 0.004 **
Happ.33:Int 3.00 0.70 8708 4.32 0.001 ***
Happ.66:Int 2.75 0.69 8708 3.97 0.001 ***
Table 2: Summary of fixed effects in arousal scores.
Coef SE df t p
(Intercept) 33.33 1.67 105 19.89 0.001 ***
Happ.33 2.42 0.55 8708 4.38 <0.001 ***
Happ.66 3.48 0.55 8708 6.31 0.001 ***
Int.Human 0.16 1.39 122 0.12 0.740
Happ.33:Int 2.39 0.78 8708 3.05 0.002 **
Happ.66:Int 2.12 0.78 8708 2.71 0.007 **
speaker and Alexa is large for non-manipulated speech, this gap
is closed in the 33 % happiness level, and the scores are virtu-
ally identical in the 66 % happiness level. Moreover, the scores
for Alexa are relatively stable, whereas the human scores rise
sharply in the 33 % happiness level. Arousal ratings (see Fig-
ure 1.C) show a different pattern: while excitement for the two
voices is equal for the non-manipulated speech (0 % happiness
level), the scores for both speakers show an increase from 0%
to 33 % and 66 % happiness levels. This increase is larger for
the human, relative to the Alexa, voice.
As for the anthropomorphism scores, we observed varia-
tion across participants (mean 131.9 ±71.2, range 0 - 300). In
both mixed effects models, there were interactions between AN -
THROPOMORPHISM SCORE and HAPPINESS LEVEL. Figure 2
illustrates the anthropomorphism scores in the different condi-
tions. In the valence model, participants with higher anthropo-
morphism scores rated the Alexa voice as sounding more posi-
tive at the baseline happiness level, 0% [Coef = 0.02, SE = 5.2e-
03, t= 3.1, p< 0.01]. No other interactions were observed. In
the arousal model, a higher anthropomorphism score was asso-
ciated with less perceived excitement at the highest happiness
level, 66 % [Coef = -0.02, SE = 6.0e-03, t= -3.2, p< 0.01]; no
other interactions were significant for the arousal model.
4. Discussion and Conclusion
Overall, we found that listeners perceive emotion gradiently in
both human and voice-AI (here, Amazon’s Alexa) voices. How-
ever, this was limited to arousal ratings for the Alexa voice,
while both valence and arousal ratings for the human voice rose
with the increasing ‘happiness’ manipulations. This finding
is broadly in line with the CASA theoretical framework [11,
10], as the subjects were hearing different levels of ‘excite-
ment’ in both a human and an Alexa voice that were manip-
ulated identically. Yet, these findings also illuminate a possi-
bly limited aspect of technology personification: listeners heard
variation in valence in the human voice, but not for the Alexa
voice. This might indicate that users still do not expect – or
are not used to – TTS voices that show this dimension of emo-
tion. Another factor may be the nature of the task. Listen-
ing tasks are somewhat passive comparing to the typical use
Anthropomorphism by arousal/valence
arousal
valence
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Mean rating (0-100)
Figure 2: Effect of anthropomorphism scores on perceived Hap-
piness Level (0, 33, 66 %) on valence (top panel) and arousal
(bottom panel) ratings of the Alexa (blue solid line).
of voice-AI personal assistants. It is possible that the range of
listeners’ emotion judgments would be wider in more natural-
istic, dyadic interactions. Future work exploring emotion per-
ception across different types of interactions (e.g., more func-
tional, more social) are needed to further explore this effect.
Additionally, we found evidence that individual variation
in anthropomorphism of voice-AI mediates emotion perception
of the Alexa voice: participants who displayed greater person-
ification of the Alexa voice rated it as being more positive at
baseline, while also rating the voice as sounding less excited at
the 66 % happiness level. Our valence anthropomorphism find-
ings, (i.e., participants who personify Alexa more tend to also
rate the voice as sounding happier) are in line with research sug-
gesting greater generalization of positive attitudes (here, more
human-like qualities) to other domains [32]. While the decrease
in arousal ratings was unexpected, the lack of correspondence
between the valence and arousal results are consistent with prior
work showing their separable effects, which are further affected
by patterns of individual variation (e.g., personality, cultural
background; cf. [33]). One limitation in this study is that the
participants were not balanced by gender, with far more female
than male raters. While we made no a priori hypotheses about
how individuals might respond differently according to their
gender, this may be a source of variation [34]. Future work
examining different types of emotion, as well as comparing in-
dividuals of different linguistic/cultural backgrounds, genders,
and even ages can further our understanding of sources of vari-
ation in the relationship between voice-AI/human emotion per-
ception and anthropomorphism.
Overall, our findings suggest that the way humans engage
with voice-AI systems is similar in some ways to humans – in
perceiving increases in ‘arousal’ – but perception of emotion
multidimensionality (i.e., both valence and arousal) appears to
be limited to natural human productions.
5. Acknowledgments
This material is based upon work supported by the National
Science Foundation SBE Postdoctoral Research Fellowship un-
der Grant No.1911855 to MC. Funded in part by the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation)
– Project-ID MO 597/6-2 and STE 2363/1-2.
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1822
... Furthermore, the extent to which individual variation by humans' social and cognitive characteristics shapes speech adaptation to voice-AI is a promising area for future research. Prior work has shown variation in how people perceive and personify technological agents, such as robots (Hinz et al., 2019) and voice-AI (Cohn, Raveh, et al., 2020;Etzrodt & Engesser, 2021). Recently, some work has revealed differences in speech alignment toward voice-AI by speaker age (e.g., older vs. college-age adults in Zellou, Cohn, & Ferenc Segedin, 2021) and cognitive processing style (e.g., autisticlike traits in Snyder et al., 2019), suggesting these differences could shape voice-AI speech adaptation as well. ...
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