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A Large-Scale User Study of an Alexa Prize Chatbot: Effect of TTS Dynamism on Perceived Quality of Social Dialog



This study tests the effect of cognitive-emotional expression in an Alexa text-to-speech (TTS) voice on users' experience with a social dialog system. We systematically introduced emotionally expressive interjections (e.g., "Wow!") and filler words (e.g., "um", "mhmm") in an Amazon Alexa Prize socialbot, Gunrock. We tested whether these TTS manipulations improved users' ratings of their conversation across thousands of real user interactions (n=5,527). Results showed that interjections and fillers each improved users' holistic ratings, an improvement that further increased if the system used both manipulations. A separate perception experiment corroborated the findings from the user study, with improved social ratings for conversations including interjections; however, no positive effect was observed for fillers, suggesting that the role of the rater in the conversation-as active participant or external listener-is an important factor in assessing social dialogs.
Proceedings of the SIGDial 2019 Conference, pages 293–306
Stockholm, Sweden, 11-13 September 2019. c
2019 Association for Computational Linguistics
This study tests the effect of cognitive-
emotional expression in an Alexa text-to-
speech (TTS) voice on users’ experience
with a social dialog system. We
systematically introduced emotionally
expressive interjections (e.g., “Wow!”)
and filler words (e.g., “um”, “mhmm”) in
an Amazon Alexa Prize socialbot,
Gunrock. We tested whether these TTS
manipulations improved users’ ratings of
their conversation across thousands of real
user interactions (n=5,527). Results
showed that interjections and fillers each
improved users’ holistic ratings, an
improvement that further increased if the
system used both manipulations. A
separate perception experiment
corroborated the findings from the user
study, with improved social ratings for
conversations including interjections;
however, no positive effect was observed
for fillers, suggesting that the role of the
rater in the conversationas active
participant or external listeneris an
important factor in assessing social
1 Introduction
Dialog systems, despite recent improvements,
still face a fundamental issue of how to convey
interest and emotion via text to speech (TTS)
synthesis. Many TTS voices have been described
as “robotic” or “monotonous” by human listeners
(Baker, 2015), an issue further exacerbated for
generation of longer utterances (Németh et al.,
2007). This is particularly relevant for non-task-
oriented dialog systems, such as those that aim to
engage users in social chitchat (Akasaki & Kaji,
2017; Liu et al., 2017); for example, Tokuhisa &
Terashima (2009) found that affective (i.e.,
emotion conveying) productions relate to
perceptions of speaker enthusiasm in non-task-
oriented human-human conversation. In another
study, adjustment of the prosodic features of
computer TTS affects listeners’ perceptions of
the system’s type of clarification request
(Skantze et al., 2006), signaling its “cognitive
state”. Still, the ability to design a computer or
robot system to convey cognitive-emotional
expressiveness remains an area of rich study in
the field of Affective Computing (AC) (cf. Tao &
Tan, 2005). While prior approaches to model
human-like expressiveness in various systems
have involved manipulation of the overall TTS
prosody, including pitch, rate, and volume (e.g.,
Gálvez et al., 2017; Henning & Chellali, 2012;
Montero et al., 1998; Mustafa et al., 2010; Nass
& Lee, 2001; Schröder, 2007), the present paper
tests whether adding minimal and discrete
emotional-cognitive expressions in a TTS voice
impacts user experience with a social dialog
system. More specifically, we examine whether
a full “overhaul” of prosody is necessary to
meaningfully improve a dialog system, or
whether we can inject units of cognitive-
emotional expression in carefully specified
locations to produce a similar effect.
Yet, our understanding of what types of TTS
modifications will result in believable and sincere
expressions of emotion and cognitive states in a
dialog system remains an open question; there
have been mixed findings as to whether “human-
like” TTS adjustments, such as adding filler
words, result in improved user metrics (e.g.,
Syrdal et al., 2010; Pfeifer & Bickmore, 2009).
Critically, the vast majority of human-
computer dialog studies have been run on a
limited number of participants and conversations
(e.g., n=96 in in Brave et al., 2005) and in a lab
setting where users are recruited to interact with
the systems (e.g., Brave et al., 2005; Cowan et
al., 2015; Qvarfordt et al., 2005; Yu et al.,
A Large-Scale User Study of an Alexa Prize Chatbot:
Effect of TTS Dynamism on Perceived Quality of Social Dialog
Michelle Cohn1, Chun-Yen Chen2, Zhou Yu2
1Department of Linguistics, 2Department of Computer Science
University of California, Davis
{mdcohn, abtchen, joyu}
2016); that is, users may not be interacting with
real intents. For one, the presence of an
experimenter could impact the way users interact
with the system (cf. Orne, 1962). This is also true
for dialog systems; users may be less comfortable
to engage in more naturalistic conversation, or
may be more willing to accept errors or
incongruencies by a computer system while in
the lab. Additionally, having fewer observations,
as well as a participant pool largely consisting of
college age students (e.g., Cowan et al., 2015)
may impact researchers ability to generalize
findings to other user demographic groups (cf.
Henrich & Heine, 2010).
In this paper, we describe an experiment
where we systematically manipulated the
Amazon Alexa TTS generation in Gunrock, the
2018 Alexa Prize winner socialbot (Chen et al.,
2018). Our participants included over 5,000 real
users who engaged with the system from their
own homes and devices. We targeted two types
of TTS manipulations: interjections (e.g.,
“Awesome!”) and filler words. We selected these
two elements as they are ways humans
communicate their cognitive-emotional states,
but vary in their intensity: while interjections
express enthusiasm and strong emotion, filler
words communicate the speaker’s cognitive
states (e.g., “Um... let me think”) in a more
tempered fashion. Both interjections and fillers
have also been proposed to serve as socio-
affective “glue” between interlocutors,
expressing emotional and cognitive states that
serve to strengthen relational bonds between
humans and computers (Auberge et al., 2013;
Sasa & Auberge, 2014; 2017).
In addition to its scope, this study is novel in
several regards. First, no prior work, to our
knowledge, has explored how individuals
respond to emotion generated by a voice-
activated digital assistant (e.g., Amazon’s Alexa,
Apple’s Siri); users may have a more personal
connection with and may even show greater
personification of these increasingly prevalent
household devices (Lopatovska, & Williams,
2018). Additionally, this paper introduces a
methodology for designing and inserting
interjections and filler words, both in terms of
their context as well as their acoustic adjustments
using Speech Synthesis Markup Language
(SSML). Furthermore, no prior experiments have
parametrically tested the presence of these two
elements in controlled studies; doing so allows us
to test whether there is a cumulative effect of
these cognitive-emotional insertions. Finally,
conducting an experiment directly through the
Alexa system is an innovative approach that
builds on past work that has largely relied on
naturalness ratings of synthetic voices with no
interactive component for the rater themselves
(e.g., Marge et al., 2010; Gálvez et al., 2017;
Hennig & Chellali, 2012; Schmitz et al., 2007).
This study can serve as a test to the ‘Computers
are Social Actors’ theoretical framework (CASA:
Nass et al., 1994; Nass & Moon, 2000) that
proposes that humans apply social norms from
human-human interaction to computers when they
detect a cue of humanity in the system. One
empirical question for the CASA framework is
what cues can trigger computer personification
and to what extent this personification graded;
that is, do we see cumulative effects of
introducing multiple human-like features in a
dialog system, or do listeners display a more
categorical response to human-likeness? In
particular, we ask whether individuals’ ratings of
social dialog quality vary according to the type
and combination of addition for interjections and
filler words.
In the following section, we will review the
literature for related work on cognitive-emotional
expression via interjections and filler words in
human-human and human-computer interaction
(HCI). Then, we will introduce our overall
chatbot dialog system design and our
interjection/filler insertion methodology in
Section 3, our user study experiment in Section 4,
and a perception experiment in Section 5.
2 Related Work
2.1 Limited Prior Work on Interjections
and Exclamations in HCI
Despite the prevalence of interjections in human
speech patterns, few groups have explored
inserting interjections in TTS systems. In human
speech, interjections constitute words or phrases
that can display emotion (e.g., emotive
interjections such as “Yuck!”; cf. Wierzbicka,
1999) or reveal the speakers “information state”
(e.g., “Aha!”). Some interjections are based on
existing words (e.g., “Neat!”), while others are
based on non-lexical vocal productions (e.g.,
“Ooh!”; cf. Yang, 2010). Interjections can also
signal that the information is newsworthy (e.g.,
“Really?” in Pammi, 2012). Still, the addition of
interjections in TTS voices remains a largely
understudied area, while much greater attention
has been given to overall prosodic adjustments
over the scope of a phrase or utterance (e.g., pitch,
duration, etc.) (e.g., Németh et al., 2007) or the
introduction of non-linguistic affective bursts in
robots (e.g., beeps, buzzes in Read & Belpaeme,
2012). While not introducing interjections per se,
but rather modeling new TTS productions based
on positive or negative interjections (e.g.,
“Great!” vs. “Oh dear!”), Syrdal and colleagues
(2010) found that speech trained on positive
exclamations resulted in higher listener ratings in
a 7-utterance simulated dialog; they observed no
such effect for TTS adjustments for negative
exclamations (e.g., “Oh dear!”, “Oops!”). One
novel line of research we explore in the present
study is whether the presence of an interjection
and the degree of prosodic dynamism in the
interjection, such as exaggerating the pitch
contour and increasing durationcontributes to a
user’s perception of the system as being more
cognitive-emotionally expressive.
2.2 Mixed Results for Fillers in HCI
Another element signaling cognitive-emotional
expression in human conversations is filler
words. In certain instances, filler words, or filled
pauses (e.g., “um”), can be considered to be a
type of disfluency or hesitation in a speaker’s
production (Clark & Tree, 2002), demonstrating
more time for the speaker to “collect” their
thoughts (cf. Brennan & Williams, 1995). At the
same time, filler words can signal information
about the speaker’s cognitive state; for example,
longer filler words have been shown to signal
greater uncertainty or degree of thought on the
conversational subject, while the pitch contour
on the filler word communicates the speaker’s
level of understanding (Ward, 2004). In some
studies, introduction of filler words in dialog
systems has a facilitatory effect on perceived
naturalness and expressiveness of the voice
(Gallé, et al., 2017; Goble & Edwards, 2018;
Marge et al., 2010; Wigdor et al., 2016). For
instance, a user’s “sensation of engagement” in a
conversation with a robot improves with the
addition of filler words (Gallé, et al., 2017).
Filler words additionally have been shown to
impact perceived likeability and engagement
with a computer, even for individuals not
directly talking to the computer/robot;
independent raters gave higher naturalness
ratings for “overheard” human-computer
conversations when the computer voice included
filler words (e.g., using the Talkie dialog system
in Marge et al., 2010).
Yet, at the same time, other studies have
reported no effect of introducing filler words
(e.g., “Hmmm”, “uh huh” in Syrdal et al., 2010),
or a negative effect for some listeners (e.g.,
Pfeifer & Bickmore, 2009). This negative
response might be expected given their
association with as markers of anxiety and
unpreparedness for some subjects. However,
Christenfeld (1995) additionally observed that
listeners’ evaluations varied based on their task:
when asked to focus on the speech style, subjects
reported more negative ratings of the filler “um”,
but subjects had no such negative judgments
when they were asked to focus on the content.
This raises an important question: how might the
experimental task impact the way users perceive
these more human-like, but in some cases more
“marked”, displays of cognitive-emotional
expressiveness? Addressing a limitation of prior
work having subjects rate stimuli presented in
isolation (e.g., Syrdal et al., 2010), our study tests
both actual user’s responses as well as external
raters in assessing the introduction of fillers.
3 Dialog System Design Amazon Alexa
Prize Chatbot
For the past two years, Amazon has launched the
Alexa Prize Socialbot Challenge to support
universities in building conversational bots to
advance human-computer interaction. General
public users with an Alexa-enabled device or free
Alexa application can access the system and talk
to the system about various topics (e.g., music,
sports, animals, movies, food, weather, etc.) in a
conversational manner. When a user engaged the
social mode by saying “Let’s chat”, one of the
socialbots in the competition was randomly
invoked. After talking to the system, the Alexa
Skill system automatically solicited user feedback
(“How likely are you to talk to this bot again, on a
scale from one to five?), providing a measure of
user engagement.
Competing in the 2018 Alexa Prize
competition, our chatbot, Gunrock (Chen et al.,
2018), aims to produce engaging and coherent
conversations with real human users. During the
competition, our bot achieved an average rating
of 3.62 (on a 1-to-5 scale) in over 40,000
conversations; conversations had an average of
18.9 turns, averaging 4.35 minutes in duration.
Our bot uses automatic speech recognition and
text-to-speech models are provided by Amazon.
It has a three-stage natural language
understanding pipeline including ASR correction,
sentence segmentation, constituency parsing, and
dialog act prediction to aid user intent detection.
Our system has a hierarchical agenda-based
dialog manager that covers different topics, such
as movies, music, etc., and a templated-based
natural language generation module that allows
the system to fill slots with data retrieved from
various knowledge sources. Please refer to Chen
et al. (2018) for system implementation details.
3.1 Methods of Inserting Interjections
We designed a framework to introduce 52
distinct interjections pre-recorded by the US
English Alexa voice actor. These interjections,
known as Speechcons (Amazon, 2018), are
“special words and phrases that Alexa
pronounces more expressively”. For a listening
sample, refer to the Speechcon website
(Amazon, 2018). We inserted these interjections
using Speech Synthesis Markup Language
(SSML) tags in the Alexa Skills Kit. These
interjections were longer in duration and showed
wider pitch variations and exaggerated pitch
contours, relative to their unmodified
counterparts (see Figure 1).
Of the 52 interjections (see Table 1 for a
breakdown), we inserted 39 phrase-initially
using a rule-based system, for the following 5
contextual scenarios, defined by conversational
template: when the bot wanted to signal interest
about the user’s response to encourage the user
to elaborate, to resolve an error, to accept a
request, to change the topic, and to express
agreement of opinion. In each context, we
randomly inserted an interjection appropriate for
that context (from the subset of pre-categorized
interjections) to increase variation and retain
user interest. Note that insertion of interjections
did not result in any pauses or other
incongruencies in the Alexa TTS generation.
Interjections were selected for each context by a
native English speaker (Author 1) based on the
acoustic production of the interjection and its
semantic/pragmatic fit in the utterance. First, we
selected positive interjections (e.g., “Wow!”)
that could be used to signal interest (Context 1)
and negative interjections (e.g., “Darn!”) in error
resolution (Context 2); we used the widest
variety of interjections for these two contexts as
these situations arose most frequently in
conversation. We denote the interjection version
of words with an exclamation (e.g.,
Context 1: To signal interest about the
user’s response and elicit user’s expansion.
We added 12 interjections phrase-initially to
show Alexa’s interest in the user’s answer
(after Alexa asks a question and the user
provides a response); these interjections
included “Awesome!”, “Cool!”, “Fantastic!”,
“Super!”, “Wow!”, “Ooh la la!”, “No way!”,
“Fancy that!”, “Interesting!”, and more (for a
full list, see Appendix A). For example:
“[Wow!… | Interesting!… | Ooh la la!…]. Tell
me more about it.”
Context 2: Error resolution. We also
introduced 14 interjections in error resolution
templates in order to show Alexa’s “feelings”
about her misunderstanding. Possible
interjections included “Whoops a daisy!”,
“Darn”, “Oh brother”. For example:
“[Whoops-a-daisy!... | Baa!... | Darn!...] I think
you said probably. Can you say that one more
Figure 1: Pitch and duration differences for
Speechcon and unmodified production of “Cool!”
generated in Praat (Boersma & Weenik, 2018).
Table 1: Total number of possible interjections
added to defined slots in conversational templates.
Context 3: To accept a request. We inserted
4 interjections phrase-initially to reflect
Alexa’s acceptance of the user’s request (e.g.,
such as to change topic), including: “Okey
dokey!”, “Righto!”, “As you wish!” and “You
bet!”. For example: “[Okey dokey!... |
Righto!... | As you wish!...] Here’s some more
Context 4: To change the topic. We used 4
interjections to transition to a new topic,
simulating a scenario where Alexa “just
remembered” something she wanted to share
with the user. We generated 2 interjection
versions of “Ooh!” and “Ah!” to use in this
context. For example: “[Ooh!… | Ah!… | All
righty!...] tell me more about you! What else
are you interested in? Do you like [music |
movies | animals]?”
Context 5: To express agreement of opinion.
We inserted 2 interjections phrase-initially to
show Alexa’s emphatic agreement to the user’s
opinion: “Yes!” “High Five!”. For example:
“[High Five!… | Yes!] We share the same
Overall, our rule-based system resulted to the
insertion of interjections in 12-18% of turns in
each conversation. We implemented these
interjections with a following pause (ranging
from 150-300ms), using SSML. Note that 13
unique interjections, of the total 52, were added
to very specific utterances (e.g., using “Moo!
with cow jokes) without using this rule-based
system (see Appendix B for stimuli and
descriptions). All the interjections were rated on
two axes by a native English speaker (see
Appendix A for full word list and classifications;
see Table 5 for an example conversation log
from in-lab user tests). Axis 1 is valence:
Positive, neutral, or negative. For example, the
interjection “Awesome!” was rated as having a
positive valence, while “Darn!” was rated as
having a more negative valence. Axis 2 is the
interjection emotional orientation: self- or other-
oriented (cf. Brave et al., 2005).
3.2 Methods of Inserting Fillers
We added 9 fillers used in American English
(Barbieri, 2008) in the conversational templates:
“um”, “hmm”, “huh”, “ah”, “uh”, “oh”, “ooh”,
“uh huh”, “mhm” (see Table 5 for an example
conversation log from in-lab user tests). In all
cases, we used SSML to add a pause (ranging
from 150-200ms) following the filler word and
slow the production of the word “so” (80% of
original rate), if it occurred before or after the
filler to improve naturalness. We added certain
subsets of filler words in three specific contexts:
to change topics, when retrieving Alexa’s
backstory, and as an acknowledgment to the
user’s utterance. Overall, this resulted in fillers
added to a total of 7.8-7.9% of total turns.
Context 1: To change topic. We added two
fillers, “um” and “uh”, either before or after
“so” to introduce a new topic. We additionally
reduced the rate of “so” (indicated by
underlining in the following examples). For
example: “[Um…sooo, |Sooo, um…| Uh… sooo
| Sooo… uh,] I've been meaning to ask you: do
you like to play videogames?
Context 2: When retrieving Alexa’s
backstory. We added six fillers (“mhmm”,
“hmm”, “um”, “uh”, “oh”, and “ooh”) at the
beginning of the utterance when the user had
asked Alexa a question, simulating that Alexa
needed time to consider her own experience
and/or opinions. For example: “[Hmm…, |
Uh | Oh | Ooh…| Mhmm…] I love all
animals, but I think my favorite is probably the
Context 3: As an acknowledgment to the
user’s answer to Alexa’s question. We added
the fillers to act as feedback response tokens.
Specifically, we added “ah”, “oh”, “uh huh”,
“mhmm”, “huh”, and “ooh” at the beginning
of the utterance to show Alexa’s
acknowledgment of the content provided by
the user (e.g., Oh… legos? Interesting
choice!”). Note that while these utterances are
often used for backchanneling, where one
speaker provides verbal feedback while the
other continues to hold the floor (e.g., “uh
huh” in Pammi, 2012), we do not classify them
as such they did not occur during the user’s
turn. Given the limitations of the text
transcripts of the conversations—in the
absence of acoustic-phonetic data—we could
not implement a real-time backchanneling
4 Experiment 1: Chatbot User Study
In the current study, we systematically tested the
impact of adding interjections and fillers in the
Alexa TTS voice in our chatbot (Chen et al.,
2018). We h ypot he si ze t hat i n a s ocia l d ia lo g
system, adding interjections (e.g., “Awesome!”)
and filler words (e.g., “um”) in appropriate
locations, with emotional valence consistencies,
will improve overall user ratings. This prediction
stems from related work conducted in laboratory
settings with other types of interlocutors (e.g.,
robot in Gallé et al., 2017; Marge et al., 2010),
with greater expressiveness of the voice relating
to positive ratings by users (e.g. Hennig &
Chellali, 2012).
4.1 Experimental Conditions
From November 20, 2018 to December 3, 2018
we conducted an ablation study with four possible
conditions, varying according to the presence of
interjections and fillers (see Table 2). Condition A
was filtered to include interjections (and exclude
filler words). Condition B was filtered to include
filler words and exclude interjections. Condition
C included both interjections and fillers, while
Condition D excluded both elements. Condition
was randomly invoked for each user. During this
timeframe, no other code updates were
implemented. A total of 5,527 users participated
in the study for a total of 5,582 conversations,
with 62,130 conversational turns.
4.2 Statistical Analysis & Results
We modeled user rating (produced at the end of
the interaction on a scale from 1-to-5) with a
mixed effects linear regression with the lme4 R
package (Bates et al., 2015), with the fixed effect
of Condition (A: Interjection only, B: Filler only,
C: Interjection and Filler, or D: Neither) and by-
user random intercepts. Effects were contrast
coded relative to Condition D (baseline
The linear regression model revealed a main
effect of Condition on users’ ratings, with
significantly higher ratings for the three
conditions with manipulations (A: Interjection, B:
Filler, and C: Interjection & Filler) relative to
baseline (see Table 3 and Figure 2 below). The
highest rating improvement was observed for
Condition C (Interjection & Filler) with an
average increase of 0.749.
The releveled linear regression model, with
Condition C as the reference, tested whether the
combined condition (Interjections & Fillers)
showed higher ratings relative to the addition of
interjections or fillers alone. Results revealed
that Condition C indeed showed higher user
ratings than Conditions A (Interjections only:
β=-0.561, t=-26.16, p<0.001) or B (Filler only:
β=-0.326, t=-15.33, p<0.001).
4.3 Interjections Subset Analysis & Results:
We cond uc te d a mo re f in e-grained analysis on the
subset of conversations that included the
interjections (i.e., Condition A: Interjection, and
Condition C: Interjection and filler). In this
section, we test whether valence (positive, neutral,
negative), emotion orientation (self- versus other),
and interjection function (error resolution, change
Table 2: Experimental conditions & summary
Table 3:
Hierarchical linear regression model
output: User ratings based on Condition, relative
to the baseline condition (“D”).
Figure 2: Mean user rating by Condition (error
bars represent standard error; asterisks depict
significance (p<0.001) relative to the baseline
condition, “D”)
topic, signal interest, etc.) differentially affect user
ratings. We predict that more positive
interjections, interjections that communicate more
other-oriented displays of emotion, and
interjections that are used to signal interest
(relative to other functions, such as changing
topic) will show higher user ratings, in line with
prior work (e.g., Bono & Ilies, 2006 Brave et al.,
2005; Gibbs & Mueller, 1988).
A mixed effects linear regression model tested
the interjection classifications on user’s ratings.
Fixed effects included Interjection Valence
(positive, negative, neutral), Emotion Orientation
(self-oriented, other-oriented), and Context (Error
resolution, change topic, play, etc). Given the
overlap between Emotional Valence and Function
(with positive interjections exclusively used to
Signal Interest and negative interjections almost
always used in Error Resolution, see Appendix
A), we tested these two variables in separate
models. Random effects included by-user random
Model comparisons based on the corrected
AIC (Burnham et al., 2011) were conducted with
the MuMIN R package (Barton, 2017) to test the
inclusion of Emotion Valence or Function as main
effects, given their colinearity. Model
comparisons revealed that the model with the
fixed effects of Valence and Emotion Orientation
best fit the data (AICc=1689.9), relative to the
model including Function and Emotion
Orientation (AICc =1694.78). The retained model
output (see Table 4) revealed a main effect of
Emotion Orientation, with “other” oriented
emotional displays (e.g., “Wow!”) associated with
higher rating than more self-oriented productions
(e.g., “ah”). No differences were observed on the
basis of interjection Val e nce.
4.4 Qualitative User Study
As part of the Alexa Prize Competition, we
additionally recruited users to interact with the
system for feedback and bug testing for earlier
versions of the dialog system. In September and
October 2018, we recorded the interactions of
twenty volunteers (12 undergraduates, 8 graduate
students). After talking to the socialbot, subjects
were asked about their interaction. Several
subjects mentioned that they liked the filler words
in Alexa’s speech as it “sounded like she was
actually thinking” or “seemed more realistic”.
Additionally, we noted that subjects often laughed
or smiled when they heard the hyper-expressive
interjections while they were part of the
conversation (e.g., “Wowza !”).
5 Experiment 2: Perception Study
While our user study suggests an improvement on
the basis of interjections and fillers, it is possible
that other factors played a role in the final ratings
(e.g., specific phrasing), as well as the co-
occurrence of certain interjections, with particular
dialog acts (e.g., Alexa using “Darn!” to resolve
errors). To disentangle these factors, we
conducted a psycholinguistic experiment using a
Table 4: Mixed effect linear regression output for
interjection classification effects on user ratings.
Sooo…umm… (Filler), have you seen any
really is an exceptional movie! Here is
Table 5: Conversation transcription from an in-
lab user test. Interjections and filler words are
denoted by italics and labeled (original,
Qualtrics survey administered through Amazon’s
Mechanical Turk1.
5.1 Participants, Stimuli, and Procedure
A tot al o f 85 Amazon Mechanical Turk workers
(i.e., “Turkers”) participated in the rating task
(note that all Turkers had to have an approval
rating of 97% or higher and at least 1000 prior
HITs). Stimuli consisted of four 3-utterance
dialogs between Alexa and a human male talker
(a native English speaker, age 29). The
conversation topics were based on those discussed
in the main social bot (animals and movies),
though were novel utterances. The dialogs
systematically varied as to whether the expression
of emotion in the interjection (if expressed) was
self- or other-oriented and had positive or
negative valence.
Using the rules for inserting interjections and
fillers (see Sections 3.2 and 3.3) and mirroring the
Condition structure from Experiment 1, we
systematically generated four conditions for each
dialog: A) Interjection addition, B) Filler addition,
C) Interjection and Filler addition, and D)
Baseline. In each of these conditions, we held the
human’s response exactly the same, as well as all
of the wording (for an example, see Table 6).
Using a between-subjects design, we additionally
tested whether the conversational context for filler
words in the first utterance affects their ratings
(e.g., following: “So” versus “Yeah, movies can
be really fun….So”).
In the experiment, subjects heard each
utterance (randomly presented) and were asked to
rate Alexa on several dimensions using a sliding
bar (on a scale of 0-to-100): likeability,
naturalness, expressiveness, and engagement
(e.g., “How engaged does Alexa sound in the
conversation?”). Two listening comprehension
questions were included to ensure that Turkers
were attending to the stimuli and task at hand
(e.g., What was Alexa’s favorite animal?
Correct response: An elephant).
5.2 Analysis and Results
Subjects’ ratings for each variable were analyzed
with separate linear mixed effects models, with a
fixed effect of Condition and by-Subject random
intercepts. Results showed a main effect of
Condition, where introducing interjections
significantly increased ratings of engagement
(β=6.1, t=3.1, p<0.01), naturalness (β=3.7, t=3.5,
p<0.001), expressiveness (β=9.0, t=7.7, p<0.001),
and likeability (β=3.4, t=3.1, p<0.001) of Alexa.
Furthermore, we observed a significant
improvement of introducing both interjections and
fillers on perceived expressiveness (β=8.1, t=7.0,
p<0.001). When introducing fillers only, we
observed a negative effect on ratings of likeability
(β=-2.8, t=-2.5, p<0.05) and engagement (β=-2.4,
t=-2.1, p<0.05) (see Figure 3).
Subset analyses on interjections (Conditions B
and C) relative to the baseline were conducted to
test for an interaction of Condition*Orientation
(self- versus other- oriented emotion) and
Condition*Valence (positive, negative, neutral).
The models showed significant interactions for
both: interjections that were other-oriented
(p<0.001) and positive in valence (p<0.001)
showed higher ratings for likeability, engagement,
: Interjection
Alexa: So,
I’ve been meaning to ask
What else are you
interested in? Do you like
Human: I love animals!
Alexa: Aw esom e! I think my
favorite animal is the
Alexa: Sooo, um…
I’ve been meaning to ask
you. What else are you
interested in? Do you like
Human: I love animals!
: Awesome. I think
my favorite animal is the
Table 6: Example dialog (Conditions A and B)
excerpt used in the perceptual ratings study.
Interjections and fillers are annotated in italics.
Figure 3: Perceptual Ratings of Alexa for each
and expressiveness. The subset analysis testing an
interaction between the filler condition (relative to
baseline) and Conversational Context revealed no
effect on ratings.
6 Discussion
This paper combines a large-scale user study
with a targeted perceptual ratings experiment to
test the effect of adding hyper-expressive
interjections (e.g., “Awesome!”) and filler words
(e.g., “um”, "um”) in a 2018 Amazon Alexa
Prize chatbot. Overall, our user study provides
evidence that introducing these discrete
expressions of cognitive-emotional expression
improves users’ experience talking to a social
dialog system; this was evidenced by a higher
holistic rating that they provided at the end of the
interaction on a scale from 1-to-5. Using both a
large sample size and in-situ experiment of an
Amazon Alexa Skill, such that users directly
engaged with their own devices, is a novel
methodology for assessing TTS expressiveness
that extends prior in-lab studies on users
recruited to engage with the system (e.g., Brave
et al., 2005; Cowan et al., 2015; Qvarfordt et al.,
2005; Yu et al., 2016).
The cumulative effect of adding interjections
and fillers (e.g., in Condition C) suggests that
individuals might respond better to dialog
systems that use greater TTS dynamism, or
variation, in the ways in which cognitive-
emotional expressiveness is conveyed. These
findings can inform theoretical frameworks of
computer personification (Nass, 1994; Nass &
Moon, 2000); while in a conversation with the
system, users appear to be reading the minimal
and discrete “human” cognitive-emotional cues
generated by the TTS voice and these effects
are additive. Additionally, our results support the
classification of fillers and interjections as
“socio-affective glue” in developing rapport in
human-computer interaction (cf., Sasa &
Auberge, 2014).
The facilitatory effect of interjections in the
user study was additionally replicated in our
perceptual ratings study: we found higher ratings
of naturalness, expressiveness, and engagement
when Alexa used interjections (e.g.,
<speechcon>"Awesome!”</spcon>“) versus unmodified
productions of the same words (e.g.,
“Awesome.”). At the same time, we find that
introducing filler words improves ratings when
the user is directly engaging with the socialbot,
but independent raters, who are not directly part
of the conversation, give lower ratings for filler
words. This suggests that the role of the user in
the conversation, as well as the conversational
context (as being more socially oriented) may be
important considerations in evaluating TTS
manipulations to improve cognitive-emotional
Finally, this work has practical applications
for other dialog system designers, with the Alexa
system (e.g., using Speechcons), but also more
broadly. That we see an improvement across
thousands of users and unique conversations
suggests that inserting interjections and fillers
plays a key role in perceptions of social dialog
quality. We see the potential to use this
expressiveness in other types of interactions,
including task-oriented dialog (e.g., in tutoring,
counselling sessions, etc.).
7 Conclusion
Overall, we present a methodology for inserting
interjections and filler words in a socialbot
dialog system and empirical validation of their
use in a large-scale user study. In comparison to
utterance- or phrase- level prosodic
manipulations, these word-level “infusions” of
cognitive-emotional expression are easier to
implement and appear to improve users’
experience. For one, that we see an improvement
in ratings across a large-scale pool of users, each
with a unique conversation, suggests that
introducing these minimal TTS manipulations in
other types of dialog systems may be beneficial.
Future work testing the implementation of
interjections and/or fillers in task versus non-
task-oriented systems can further tease apart
their generalizability.
We would like to acknowledge the help from
Amazon in terms of financial and technical
support and Dr. Georgia Zellou for feedback on
the project.
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Appendix A. Interjection (Speechcon) Classifications
Signal Interest
Ooh la la!
Fancy that!
No way!
Resolve error
Jiminy cricket!
Whoops a daisy!
Oh boy!
Oh dear!
Oh brother!
Tsk tsk!
Ruh roh!
Uh oh!
Accept request
Okey dokey!
As you wish!
You bet!
Change topic
Spoiler alert*
(only with disclosure)
All righty!
Express agreement of
High five!
Joke (phrase-finally)
Just kidding!*
Wah wah*
Neener neener!*
Joke (specific context)
Other context and
Response to user after
telling a joke
Tee hee!^
Table A1. Interjections that are only used in very constrained contexts are annotated with an asterisk (*); those that are only
used in one, specifically specified sentence are annotated with a carat (^).
Appendix B. Methods of Inserting Sentiment-Specific Interjections
We additionally added 10 interjections in sentiment-specific utterances. These were not
interchangeable (unlike Contexts 1-4 described in Section 3.3). We used the interjection, “Spoiler
Alert!” to change the topic by leading in to a disclosure by Alexa (see example A below). We
introduced 2 interjections as a response to humor, that occurred after a response to a joke. “Tee hee!”
and “Woohoo!” (see examples B and C). We implemented “Yum!” specifically in the food module, in
response to the user’s favorite food (see example D). Similarly, we added the interjection, “Aww!” as
a response to the user disclosing information about their pet in the animal module and “Woof!” and
“Meow!” to respond if they indicated they liked dogs or cats, respectively (see examples E-G).
a) Spoiler alert!... Did you know? I am definitely more of a dog person than a cat person. How about you?
Do you like animals?
b) Woohoo!... I’m glad you get my awesome humor.
c) Tee hee!... I LOL’d at that as well | If I could giggle I would.
d) Yum!... That sounds really delicious.
e) Woof!... I love dogs.
f) Meow!... I love cats.
g) Aww!... That’s so cute.
Table B1: Examples of sentiment-specific interjections (denoted in italics).
We added several interjections (e.g., “Moo!”, “Honk!”, “Woof!”, “Just Kidding!”) at the end of
utterances to complement jokes and express playfulness (see examples G-K in Table B-2).
h) What do you call a cow during an earthquake? … A milkshake. ... Moo!
i) What do you call blueberries playing the guitar?... A jam session. ... Wah wah!
j) What did the traffic light say to the car? …Don't look! I'm about to change... Honk!
k) Why wouldn't the shrimp share his treasure?... Because he was a little shellfish... Neener neener!
l) Yeah, wouldn't it be (interesting|weird) if I could poop? ... Kerplop!
Table B2: Examples of sentiment-specific interjections (denoted in italics) added phrase-finally
Additionally, we added “Kerplop!” in our response if a user asked Alexa if she “poops” (a frequent
question in the user studies) (see Table B2 above).
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Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.
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In this ground-breaking new book, Anna Wierzbicka brings psychological, anthropological and linguistic insights to bear on our understanding of the way emotions are expressed and experienced in different cultures, languages and culturally shaped social relations. The expression of emotion in the face, body and modes of speech are all explored and Wierzbicka shows how the bodily expression of emotion varies across cultures and challenges traditional approaches to the study of facial expressions. As well as offering a new perspective on human emotions based on the analysis of language and ways of talking about emotion, this fascinating and controversial book attempts to identify universals of human emotion by analysing empirical evidence from different languages and cultures. This book will be invaluable to academics and students of emotion across the Social Sciences.
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This paper discusses an emotional prosody generator for a Malay speech synthesis system that can re-synthesize the selected vocal emotion from neutral synthesized speech output and improve the naturalness by adopting rule-based prosody conversion techniques. The role of prosodic features in emotional expression, particularly fundamental frequency and duration, has been widely investigated in several research projects. This project attempts to improve the naturalness of the synthesized emotional Malay speech by establishing an effective mechanism for the re-synthesis of emotion. Such a mechanism is created by analyzing the variation in the F0 contour of continuous emotional Malay speech against a fixed time period. The emotional prosodic generator for Malay developed in the course of this research makes use of principles of parametric prosody manipulation to synthesize four basic emotions, namely happiness, anger, sadness and fear. Subjective evaluation by means of listening tests was conducted to validate the ability of the emotions generator to generate the necessary prosody to synthesize emotional expression. The evaluation results show an overall recognition rate of between 61% and 85%.
RubyStar is a dialog system designed to create "human-like" conversation by combining different response generation strategies. RubyStar conducts a non-task-oriented conversation on general topics by using an ensemble of rule-based, retrieval-based and generative methods. Topic detection, engagement monitoring, and context tracking are used for managing interaction. Predictable elements of conversation, such as the bot's backstory and simple question answering are handled by separate modules. We describe a rating scheme we developed for evaluating response generation. We find that character-level RNN is an effective generation model for general responses, with proper parameter settings; however other kinds of conversation topics might benefit from using other models.
The aim of this preliminary study of feasibility is to give a glance at interactions in a Smart Home prototype between the elderly and a companion robot that is having some socioaffective language primitives as the only vector of communication. Through a Wizard of Oz platform (EmOz), a robot is introduced as an intermediary between the technological environment and some elderly who have to give vocal commands to the robot to control the Smart Home. The robot vocal productions increases progressively by adding prosodic levels: (1) no speech, (2) pure prosodic mouth noises supposed to be the "glue's" tools, (3) lexicons with supposed "glue" prosody and (4) subject's commands imitations with supposed "glue" prosody. The elderly subjects' speech behaviors confirm the hypothesis that the socio-affective "glue" effect increase towards the prosodic levels, especially for socio-isolated people.
Embodied computer agents are becoming an increasingly popular human–computer interaction technique. Often, these agents are programmed with the capacity for emotional expression. This paper investigates the psychological effects of emotion in agents upon users. In particular, two types of emotion were evaluated: self-oriented emotion and other-oriented, empathic emotion. In a 2 (self-oriented emotion: absent vs. present) by 2 (empathic emotion: absent vs. present) by 2 (gender dyad: male vs. female) between-subjects experiment (N=96), empathic emotion was found to lead to more positive ratings of the agent by users, including greater likeability and trustworthiness, as well as greater perceived caring and felt support. No such effect was found for the presence of self-oriented emotion. Implications for the design of embodied computer agents are discussed and directions for future research suggested.