Age-appropriate Participatory Design of a Storytelling Voice Input in
the Context of Historytelling
Torben Volkmann, Michael Sengpiel, Rita Karam and Nicole Jochems
Institute of Multimedia and Interactive Systems, University of Lübeck, Ratzeburger Allee 160, Lübeck, Germany
Keywords: Aging Users, Human-centered Design, Participatory Design, Voice Interface.
Abstract: With the demographic change, the percentage of older adults steadily increases. At the same time, new
information and communication technologies (ICT) emerge at an ever-increasing rate, making it imperative
to consider older adults in the development process to achieve the best possible usability and acceptance for
older adults. This paper describes the development of a storytelling input component in the context of
Historytelling (HT), which provides a digital interactive platform for older adults to share life stories across
generations, potentially improving their health and wellbeing. HT follows the HCD+ (Human Centered
Design for Aging) approach, claiming that older adults should be integrated as co-designers throughout the
development process. A total of 19 older adults (M=68 years old) participated in 3 studies to analyze, evaluate
and design a storytelling voice input, investigating voice communication technology for conversational
agents. They were successfully involved in the design process, with methods adjusted to accommodate
specific user characteristics of older adults and substantially contributed to the further development of the HT
project, exploring the two central research questions regarding the type of voice input suitable for older adults
and the minimal requirements for a conversational agent.
1 INTRODUCTION
With the demographic change, the percentage of
older adults steadily increases. At the same time, new
information and communication technologies (ICT)
emerge at an ever-increasing rate, making it
imperative to consider older adults in the
development process to achieve the best possible
accessibility and usability for older adults. Thus, we
should value older adults as possible co-designers in
the development process (Sengpiel et al., In Press).
The Historytelling project (HT) is a research project
relying on the strengths of older adults, giving them a
tool to tell life stories on a digital platform and share
them with other people. HT seeks to have a positive
influence on a societal, a group and an individual
level. On the societal level, HT fosters multi-
perspective historiography, on the group level
strengthening of family bonds and friendships and on
the individual level a place to actively reminisce and
reach out to others. The project addresses these
challenges by developing a digital social platform for
older adults, giving them the power to record,
visualize and share their life stories.
One key aspect of HT is the actual storytelling of
older adults. Passing on stories is mostly done via
speech as it is the most natural channel and stories are
mostly passed on in face to face conversations, having
their own research field (Bornat et al., 2015) and
potentially positive effects on the listeners (Isbell et
al., 2004). Thus, the challenge for HT is to transfer
and implement this conversational element to
technology in the best possible manner.
Thus, alongside the development of a voice input
component for HT, the goal of the research was to
explore two research questions: (Q1) Which type of
voice input is suitable for older adults? (Q2) What are
minimal requirements for a conversational agent for
older adults in the context of Historytelling?
An HCD+ (Human Centered Design for Aging)
approach focusing on participatory design and
consideration of user characteristics was used to
answer these research questions and for the actual
development of voice input for HT. Hence, older
adults took part throughout the development process.
104
Volkmann, T., Sengpiel, M., Karam, R. and Jochems, N.
Age-appropriate Participatory Design of a Storytelling Voice Input in the Context of Historytelling.
DOI: 10.5220/0007729801040112
In Proceedings of the 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2019), pages 104-112
ISBN: 978-989-758-368-1
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

1.1 State-of-the-art of Voice
Communication Technology
As Schafer (1995) pointed out, there are four
challenges regarding voice communication
technology: “(i) hardware/software implementation
of the system. (ii) synthesis for voice output, (iii)
speech recognition and understanding voice input,
and (iv) usability factors related to how humans
interact with machines”. Schafer (1995), Cohen and
Oviatt (1995) also point out advantages of voice
input: Speech is the natural way to communicate;
voice is usable even if the hands or eyes are busy;
voice communication is accessible for handicapped
persons; sometimes natural language interaction is
preferred and “pronunciation is the subject matter of
computer use” (Cohen and Oviat, 1995).
In the last few years, different digital voice
assistants such as Google Home and Amazon’s Echo
were developed and marketed that increased the
overall usage of voice input systems. Thus, the
longstanding problem of speech recognition and
understanding voice input seems to have been solved
for the consumer market, at least in a narrowed
context (Hailpern et al., 2010; Hazen et al., 2004;
Levin and Lieberman, 2004). Especially new
developments of neural networks bring constant
improvements to the field of voice recognition (Arik
et al., 2017).
Technologically, there are three options to
process the voice input: audio recording, speech-to-
text input and automatic transcription. Audio
recording is possible using various ICT, such as
laptops, tablets and smartphones. The speech-to-text
input converts spoken words instantaneously into
text, whereas the automatic transcription converts the
recorded audio to text afterwards and is often used for
automatic interview transcription.
1.2 State-of-the-art of Embodied
Conversational Agents
With a strong focus on reminiscing and passing on
life stories, it is most likely that HT will provoke
emotional reactions during the process of telling and
listening to stories. Thus, it is important to design an
interface that responses to these reactions. One
possibility to do so is by using avatars, which can
answer to emotional stories via facial expressions and
gestures (Sutcliffe, 2017). Using the OCC (Ortny,
Clore, Collins) model, Sutcliffe (2017) proposes a
taxonomy based on 22 emotions, split into reactions
to events, agents (other people) and objects to design
suitable reactions of systems.
Integrating these emotions via faces can be done
with embodied conversational agents (ECA), which
gained attention in the last few years in research
(Tsiourti et al., 2014). ECAs are virtual characters,
which have the same properties as humans in a face-
to-face communication and have been successfully
integrated into projects with older adults (Cassell,
2000). It became apparent, that older adults followed
instructions by ECAs better than those by classic user
interfaces and that they had a subjectively had a
positive influence on recall tasks (Ortiz et al., 2007;
Tsiourti et al., 2018).
Isbister and Doyle (2002) developed a taxonomy
relevant for the development of an ECA. It consists
of five different categories to classify and evaluate
ECAs: Believability, Social interface, Application
domains, agency and computational issues and
production.
1.3 Participatory Design Process
Participatory design is often seen as a third space of
human computer interaction in which the knowledge
of different stakeholders such as the user and the
developer can be combined, giving new insights to
perform new actions (Muller, 2003). Thus,
fundamental design decisions are based on
information gathered by involving potential users into
the discussion about functionality, features and look-
and feel. Participatory design especially helps if the
developers are not specialists in the observed field.
There are special demands for participatory
design methods involving older adults. For them,
some conventional design methods may even be
inappropriate (Eisma et al., 2004).
In a literature review, Orso et al., (2015) found
that especially visual prompts (graphical
representation of an abstract concept), experiencing
(giving a direct first-person perspective, i.e. with
video sketches), hands on (evoking the reaction and
opinion on a tool by providing a physical object
instead of a conceptual prototype) and natural tasks
(performing a task that is similar to the final context
of use) are used when older adults are involved in
designing interactive technology. For the HT-
development, the HCD+ approach was used,
emphasizing the importance of involving the user in
every crucial design step as participatory designers.
HCD+ especially provides guidelines regarding the
recruitment of participants, the atmosphere when
working with older adults and required adaptations
concerning the concrete execution of methods
(Sengpiel et al., In Press).
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105

In the analysis phase, current technological
approaches were tested and evaluated by older adults.
In the design and conception phase, an experimental
game was conducted to develop specific design
elements. As a last step, a task-based evaluation of the
developed interface was conducted.
2 VOICE INPUT ANALYSIS
2.1 Method
To answer the first research question (“Which type of
voice input is suitable for older adults?”), an
evaluation of state-of-the-art software was conducted.
Thus, the three different input technologies were
subjectively evaluated.
Interviews are an important method in an HCD
development process, especially in the beginning
(Wood, 1997). Due to the potential lack of computer
literacy in the group of older adults (Fisk, 2009;
Sengpiel and Dittberner, 2008), a task-based
evaluation of various technologies was conducted in
this initial study.
In the evaluation, eight older adults aged between
60 and 73 (M=67.5, SD=3.7) took part. Four of them
were males and four females. They were recruited
through personal contacts, mailing lists and notice
boards. Seven interviews took place at the university,
one took place at home due to physical handicap.
The evaluation was divided into three parts:
introduction, practical work, and follow-up. In the
introduction, participants introduced themselves and
where asked about key aspects of their life and
technology usage. In the practical work phase, the
older adults got a task for three different input
approaches. Google docs was used to demonstrate the
speech-to-text capabilities, the software “Speak a
Message” was used for audio recording and
transcription. Qualitative post-interviews were
conducted after every task.
As a follow-up, each participant was asked for
their favorite input approach and filled in a
questionnaire testing their computer literacy
(Sengpiel and Dittberner, 2008) and affinity for
technology (Franke et al., 2018)
2.2 Results
In particular, the transcription method was not well
known among the participants or they had outdated
information on technical possibilities and were
positively surprised about the initial quality of the
automatic transcription.
All participants stated that an assistive system and
better feedback by the software would be appreciated.
The preferred feedback varied among the
participants, so that visual and auditory assistance
should complement each other.
The results show a strong heterogeneity within the
group of participants regarding affinity towards
technology and computer literacy. Thus, some
participants were confident in using the presented
software, whereas others needed some time to adjust
to the task. Faster participants showed a higher
affinity towards technology and computer literacy
and stated that they tend to find solutions on their own
when problems occur.
All (N=8) participants had either a laptop (6) or a
computer (3) at home and used either a smartphone
(5) or a cell phone (3). They used computers mainly
for word processing, mailing and targeted
information searching, with a weekly average time of
M=18.9 hours (SD=7). On average, they scored
M=20.4 (SD=4.17) on the computer literacy scale
(CLS, max = 26), which is still low compared to a
younger group (M=23.9), but relatively high
compared to other older adults (M=14.4, Sengpiel
and Dittberner, 2008). Also, they scored M=2.8 on
the affinity for technology interaction scale (ATI,
SD=0.9, max score = 6).
The participants stated that their technical
difficulties were situational and rather hard to
describe. When problems occurred, they would
mainly turn to friends or family or seek professional
help. Three participants stated that they try to find the
solution on their own first. However, they also desire
assistance provided by the device itself.
Alternatively, integrated tutorials as videos would be
appreciated, an approach that has been described by
Sengpiel and Wandke (2010) among others. The
practical part of the study could only be conducted
with 7 of the 8 participants.
2.2.1 Speech-to-Text
Five of the seven participants had never used speech-
to-text input, and even the two participants who had
used this technology before were surprised by the
accuracy of the results.
Three participants stated that the conversion from
speech to text was too slow, impairing oral fluency.
Also, some problems with speech were ambiguous or
not seen at all. The software was not “user friendly”,
since finding functionality was difficult and it was not
clear when the recording had started.
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2.2.2 Speech-to-Audio
Six of the seven participants had used a dictation
device to record audio before. Foremost, participants
liked the simplicity of that method, the possibility to
replay and edit the audio footage later, and the fact
that audio authentically captures the atmosphere.
2.2.3 Transcription
None of the participants had used audio transcription
before, but five out of seven participants liked the
possibility to have both, audio and text. Assessed
particularly positively was the unobtrusiveness of the
method, maintaining the oral fluency. Nonetheless,
the quality of the initial transcription is crucial for
further adoption.
2.2.4 Preferred Input Method
The overall quality and usability aspects of each
method played a big role in participants' preferences.
Furthermore, intended audience and purpose are key
drivers of the preferred method. If the goal was to
write a short story quickly, participants would choose
the speech-to-text input. The transcription technology
was preferred especially for longer, more meaningful
stories. Table 1 shows the acceptance among the
participants, multiple answers were possible. Since
older adults preferred the transcription technology, it
will be used for further development.
Table 1: Acceptance frequencies of input methods among
participants (N=8).
Technology Acceptance frequency
Transcription 6
Audio recording 2
Speech-to-text 4
3 AGE-APPROPRIATE DESIGN
3.1 Methods
To answer the second research question (“What are
minimal requirements for a conversational agent for
older adults in the context of Historytelling?”) a
workshop with three different groups was conducted.
Due to possibly low computer literacy among the
participants, the technology was partly replaced by a
real-life example. (see also Lindsay et al., 2012;
Sengpiel et al., In Press)
There are a variety of methods using real life
examples as prototypes for technology development,
among them „invisible technology videos“ (Lindsay
et al., 2012), (Cultural) Probes (Brandt and Grunnet,
2000), and Forum Theater (Rice et al., 2007).
We used a simulation game often used in
educational context, more specifically a modified
simulation game used by Reich (2007).
He states that the ideal simulation game consists
of seven phases: (1) introduction, (2) information and
reading, (3) opinion-forming and strategy planning,
(4) interaction within the groups, (5) preparation of a
plenum, (6) conducting a plenum, (7) game
evaluation. Due to a lack of time, the second and third
phases were omitted during the workshop and
conducted a priori by the researchers. Phase seven
was conducted by the researchers after the workshop.
We recruited nine older women (M=68) through
the “Deutscher Frauenring e.V.”, a leading women's
organization in Germany, who took part in three
rounds within a larger full day workshop with several
parts on the University campus.
To record interactions, we used a desktop
microphone and the software “Speak a Message”
running on a laptop with external screen and mouse.
The simulation game lasted 15 min per round plus
seven minutes for discussion. Participants were inter-
viewed afterwards according to their respective roles:
Assistant (Please simulate a voice assistant.
Remain within your role and react to anything you
notice.)
Storyteller (Please read out loud this shortened
version of “Mother Hulda”. The assistant will help
you with the recording.)
Observer (Please observe the interaction between
the assistant and the storyteller and fill in this
observation sheet.)
The assistant and the storyteller were positioned
to have no direct eye contact, while the observer was
asked to sit seeing both (see the sketch and photo in
figure 1).
Figure 1: Sketch and photo of the simulation game's setup;
A=Storyteller, B=Assistant, C=Observer, D=examiner.
Age-appropriate Participatory Design of a Storytelling Voice Input in the Context of Historytelling
107

3.2 Results
The use of the simulation game method showed that
participants were good at taking the provided
perspectives, were eager to give meaningful
information and help with their expertise and had no
problems solving the tasks given.
In the follow-ups there were lively discussions
about possible improvements, which will be
translated into requirements for the assistance system.
3.2.1 Participants
All the 9 older participants were women. Eight out of
nine older adults were using their computer or laptop
frequently, all participants own a smartphone and
seven out of nine used it frequently. Technology is
mostly used for communication and targeted
information research. As expected for their age group,
they scored relatively low on the computer literacy
scale (CLS: M=16, SD: 3.67) but high on the affinity
for technology scale (ATI: M= 3.8, SD=0.8).
3.2.2 Simulation Game
Simulation game results are quite diverse between
groups, for they showed very different behavior. For
example, group 2 had a fluent dialogue, while the
other groups had rather functional dialogues, e.g.:
Group2: Assistant: “I am the voice assistant. My
name is…“ Storyteller: “I am the storyteller. My
name is… and I will start right away.”
Groups 1 & 3: Assistant: “I am the voice
assistant. My name is… Have you turned on your
microphone?” Storyteller: “Yes, should I press the
record button? Assistant: “Yes “
Group 1 did not establish a fluent dialogue, yet in
the interview the storyteller said she would have liked
a more fluent dialogue and better feedback from the
assistant, especially regarding recording quality.
Group 2 established a fluent dialogue from the
start and immediately reacted to the assistant’s
remark to speak louder. However, in the interview the
storyteller considered this interruption unpleasant and
said she would prefer visual help and remarks, for any
interruption in the flow of storytelling should be
avoided.
Group 3 started with a longer dialogue, but the
storyteller had forgotten to record it. The assistant
said in the interview that she had noticed it, but did
not want to interrupt the storyteller, conceding
afterwards that it would have been better to do so.
They also appreciated the dialogue in the beginning
and wished it could have been continued in the study
as well as with the technical system to be developed.
3.2.3 Resulting Interface Requirements
With the simulation game, some requirements were
developed for the assistance system: It should answer
user questions with a fluent verbal dialogue, being
able to assess events' relevance and adapt kind and
timing of communication to avoid unnecessary
interruptions. In essence, the participants hoped for an
assistance system behaving like a polite competent
human, perhaps pushing the boundaries of today’s
technology.
Especially the recording flow should be supported
from start to finish. There are further requirements for
voice input communication in the literature, which
were pointed out in 1.1
3.3 Resulting Interface
The resulting high-fidelity prototype is based on an
interface presented in an already published paper
(Volkmann et al., 2018) to ensure consistency within
the HT project. Since our prototype could not display
dynamic content, some interface elements had to
remain static. Thus, some interactions such as
providing feedback in recording sessions were
triggered by the experimenter as Wizard of Oz. There
were four kinds of feedback:
• A visualization based on a VU (volume
units) meter which is a standard display for
the signal level in audio equipment (see
figure 2).
• Warning messages (see figure 3).
• An earcon (ear + icon) which are “abstract,
synthetic and mostly musical tons or sound
patterns that can be used in structured
combination” (Dingler et al., 2008).
Figure 2: VU meter used for audio visualisation.
Figure 3: Warning message.
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Figure 4: Voice assistant Lisa speaking.
• A voice assistant in form of an ECA as
described in 1.2 (see figure 4).
The assistance is provided in three standardized,
consecutive steps. First, a problem in audio quality is
visualized through the VU meter. If the user does not
perceive the problem and thus does not deal with it,
an earcon is played and an additional warning
message was displayed that the recording will be
stopped. For the last step, the assistance varies. In a
first implementation, there is no additional warning.
In a second implementation, an avatar is used to give
the information about the problem.
4 EVALUATION
To assess usability and user experience of the
interface, a wizard of oz evaluation was conducted.
The participants were given the task to record a story
with the provided interface and assistance was
provided as described in 3.3.
Two questions were essential for the evaluation:
(1) Has the assistance been perceived? (2) Which
assistance was preferred?
In the evaluation, eight older adults aged between
61 and 70 (M=66, SD=3) took part, five were male
and three female. Six participants had already
participated in the first study. They were recruited
through personal contacts, mailing lists and notice
boards. All evaluations took place at the university.
To give the evaluation an attractive context, a
Christmas flower and a candle were placed around the
participants, among other things. Also, cake, water
and hot drinks were served (Newell et al., 2007).
Figure 5 shows part of the room in which the
evaluation was conducted. Behind the participant (A),
the wizard (B) and the recorder (C) were present in
the room.
First, the participants were handed out a
questionnaire regarding demographic information,
affinity for technology (Franke et al., 2018) and
computer literacy (Sengpiel and Dittberner, 2008).
Then, the participants were confronted with the voice
input interface. The order of assistance use was
randomized. Before each run, the microphone was
secretly placed too far apart from the participants
creating a problem with audio quality, to justify the
system warning and trigger a response from the
participants. Before the run of the classic interface,
also the microphone cable was unplugged. After the
recorded interaction with the interface, the User
Experience Questionnaire (UEQ) (Schrepp, 2015)
was filled out by the participants. The second
interface was tested correspondingly. In a post-
interview possible adjustment possibilities and
preferred interfaces were discussed.
4.1 Results
Overall, the Wizard of Oz prototype proved well
suited to test the functionality that would have been
hard to implement, such as the transcription or the
avatar, although wizard response time was sometimes
too high to satisfy the participants. The rather simple
prototype allowed for participants’ immersion in the
process of storytelling.
4.1.1 Participants
Of the 8 older participants (61 – 70 years, M=66,
SD=3), 5 were women and 3 were men. They mainly
used computers, tablets and smart phones, mostly for
text editing, email, internet searching and surfing. For
their age group, they had relatively high computer
literacy (CLS: M=21.8, SD: 4.1) and high affinity for
technology (ATI: M= 3.1, SD=1.1).
4.1.2 Awareness of Provided Feedback
The participants used the provided VU meter for
regular monitoring. The earcon was often ignored or
not perceived at first, especially by the participants
immersed in the storytelling. Three out of eight
participants perceived the earcon from the start, three
other participants perceived it the second time. It
seems reasonable to assume that earcons need to be
learnt before (Dingler et al., 2008).
All participants perceived the information the
voice assistant gave about occurring problems, but
they did not engage in conversation.
A combined approach considering the importance
of intervention might work best in this scenario. It is
generally difficult to give the storyteller information
about options to improve the quality of the audio
signal, while maintaining the oral fluency of the
storytelling process.
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109

Figure 5: Sketch and photo of the evaluation's setup.
4.1.3 Preferred Assistance
The User Experience Questionnaire (UEQ) revealed
a strong preference for voice assistance, but only if
feedback was triggered on time. If it was delayed,
then discomfort, confusion, and frustration occurred,
and participants rated the User Experience much
lower in all UEQ categories. However, using the
interface without voice assistant, delays had much
smaller impact on the UEQ score. Figure 6 shows this
interaction effect for voice assistant x delay based on
UEQ mean scores across the scales found in Table 2.
Table 2: Results of the User Experience Questionnaire
(Scale ranging from -3 to +3) for recordings with and
without voice assistant either delayed or on time, indicating
an interaction effect (see figure 6).
On time (N=5) Delayed (N=3)
Aspect M SD M SD
Recordings with voice assistant
Attractiveness 1.37 1.1 -0.7 0.8
Perspicuity 1.5 0.8 -1.2 0.6
Efficiency 1.3 0.7 -0.3 0.3
Dependability 0.8 0.8 -1.2 0.4
Stimulation 1.15 0.5 0 0.8
Novelty 0.85 0.6 0.2 0.7
Recordings without voice assistant
Attractiveness 0.77 1.0 0.3 1.0
Perspicuity 1.25 1.3 1.0 0.9
Efficiency 0.95 0.8 1.0 0.7
Dependability 0.35 1.3 0.5 1.1
Stimulation 0.95 1.3 0.3 1.2
Novelty 0.65 0.4 0.0 1.0
5 DISCUSSION
Following the HCD+ approach, possible future users
were integrated in all steps of the development of the
voice input component and methods were adjusted to
accommodate user characteristics of older adults.
Although the computer literacy score was rather high
compared to other groups of older adults, due to the
large heterogeneity within groups the adjustments
were beneficial to the goal of universal usability, not
to preclude anyone by design.
Regarding the first research question “Which type
of voice input is suitable for older adults”, the
subsequent transcription was preferred among the
participants. Participants wanted to have both, text
and recorded audio, which can be achieved by the
transcription. However, the quality of the text to
speech engine used in the tested software was not
sufficient to maintain uninterrupted oral fluency and
there were still errors in the transcript. Additional
studies have to be conducted to assess the maximum
acceptable fault tolerance and if current technology
can undercut this line. If that is not possible with
current technology, we suggest weighing the potential
benefits to a loss in user experience due to user’s
frustration.
Figure 6: Interaction effect for voice assistant x delay on
UEQ mean scores (see table 2 for details).
For practical purposes, the HT system could
estimate in the beginning, whether recordings with
voice assistant could be delivered without noticeable
delay and conceal it otherwise to avoid the “UX
penalty” for a delayed voice assistant shown in figure
6 and table 2. Audio files could be stored and
transcribed later (with enhanced technology) as well.
In the HT context, volunteers might also be willing to
correct errors in the transcripts for the storytellers.
Regarding the second research question on
minimal requirements for a conversational agent for
older adults in the context of Historytelling, users
should be guided through the recording process. A
virtual speech assistant giving necessary information
could be helpful, but it should recede into the
background during story recording and graphical user
interface elements should be used for regular
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monitoring instead. Again, a delay in the assistants’
feedback should be avoided, because it cripples user
experience, inverting the benefits of conversational
agents and leaving the users uncomfortable and
confused.
ACKNOWLEDGEMENTS
We would like to thank Dr. Daniel Wessel for his help
planning the evaluation and the many participants
who sacrificed their spare time for the Historytelling
project. The HCD+ approach would never work
without them.
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