TipTopTalk! Mobile application for speech
training using minimal pairs and gamiﬁcation
Cristian Tejedor-Garc´ıa1, David Escudero-Mancebo1,
C´esar Gonz´alez-Ferreras1, Enrique C´amara-Arenas2, and
1Department of Computer Science
2Department of English Philology
University of Valladolid
Abstract. This demonstration describes the TipTopTalk! mobile appli-
cation, a serious game for foreign language (L2) pronunciation training,
based on the minimal-pairs technique. Multiple Spoken Language Tech-
nologies (SLT) such as speech recognition and text-to-speech conversion
are integrated in our system. User’s interaction consists in a sequence of
challenges along time, for instance exposure, discrimination and produc-
tion exercises. The application implements gamiﬁcation resources with
the aim of promoting continued practice. A speciﬁc feedback is also given
to the user in order to avoid the performance drop detected after the
protracted use of the tool. The application can be used in diﬀerent lan-
guages, such as Spanish, Portuguese (European and Brazilian), English,
Chinese, and German.
Keywords: serious game, speech technology, computer assisted pronun-
ciation training, gamiﬁcation, learning analytics, L2 pronunciation, min-
There are many software tools that rely on speech technologies for providing to
users L2 pronunciation training in the ﬁeld of Computer Assisted Pronunciation
Training (CAPT). While such tools undoubtedly engage users in learning-
oriented practice, there have been very few attempts to objectively assess the
actual improvement attained by them . The volume of technological services
for smartphones and other smart devices is growing everyday . Currently the
most popular mobile and desktop operating systems grant users a free access to
several Text-To-Speech (TTS) and Automatic Speech Recognition (ASR) sys-
tems. Besides, the combination of adequate teaching methods and gamiﬁcation
strategies will increase user engagement, provide an adequate feedback and, at
the same time, keep users active and comfortable .
This paper describes the software tool TipTopTalk!1 a second gen-
eration serious game application designed for L2 pronunciation training and
2 TipTopTalk! Mobile application for speech training
testing. It is a two-years project focused on advanced research in speech train-
ing technology, such as speech recognition and text-to-speech conversion and
the successful joint integration of them in a multilingual and multimodal infor-
mation retrieval system. The languages considered in the project are Spanish,
Portuguese (European and Brazilian), English, simpliﬁed Chinese, and German.
The rest of the paper is structured as follows. Section 2 oﬀers an overview of
our system, the application dynamics and the user interface. Section 3 describes
the demonstration’s script. Finally, section 4 provides the conclusions and future
2 Description of the system
2.1 General overview of TipTopTalk!
Three main elements are involved in our system, an Android client application,
an own web server and external services provided by Google. See references
 for more speciﬁc details. Figure 1 represents the conceptual architec-
ture of the Android client application. The Control module includes the appli-
cation’s business logic. The minimal pairs database is accessed by the Control
component in order to extract the minimal pairs lists of each language. The
Game Interface component presents each pair to the users in accordance with
the game dynamics. The Control component makes use of an ASR component
that translates spoken words into text. When the patterns produced by the ASR
component match those of the target words, the pronunciation is correct. The
TTS component is used to generate a spoken version of any required word. It
allows users to listen to a model pronunciation of the words before they try to
pronounce them themselves. We use both Google’s free ASR and TTS system.
However, TipTopTalk! adapts to any ASR or TTS that works with Android.
AConﬁguration component selects the language in which the ASR and TTS
components operate. Furthermore, it allows selecting among diﬀerent sets of min-
imal pairs according to the language to be tested. Results will show the capital
importance of a proper selection of minimal pairs. The minimal pairs database
–which constitutes the knowledge database of the system– can be updated in
order to improve the system or to include new challenges.
Finally, a Game Report is generated at the end of each game. This report reg-
isters user dynamics, including the timing of the oral turns (both for recognition
and for synthesis) and the results obtained. We gather relevant quantitative data
from all emerging events in the visual interface of the application with which we
feed a daily log for each user in order to determine whether her or his pronunci-
ation skills are improving. In addition, we send depersonalized user’s interaction
events to our Google Analytics account in order to compute how often a given
event has occurred.
2.2 Pedagogical activities cycle
TipTopTalk! follows a learning methodology based on the sequencing of three
diﬀerent learning stages: exposure, discrimination and pronunciation . It relies
TipTopTalk! Mobile application for speech training 3
Fig. 1. Conceptual components of the client’s system.
on the use of minimal pairs. They raise users’ awareness of the potential risks of
generating wrong meanings when phonemes are not properly produced. The
lists of minimal pairs used by the tool are selected by expert linguists in order
to obtain the best possible results. TipTopTalk! tries to adapt this methodology
with gamiﬁcation elements since it is a serious game.
As a consequence, there are three main game modes. The ﬁrst one is the
exposure mode, players become familiar with the distinctive phonemes within
sequences of minimal pairs selected by a native linguist and presented at random.
The aural correlate of each word is played a maximum of ﬁve times. Then, users
decide whether to move on to next round of words, or to record their own
realization of the words to compare it with the TTS version.
Secondly, in the discrimination mode, users test their ability to discriminate
between the elements of minimal pairs. They listen to the aural correlate of
any of the words in each pair and must match it with the correct written form
on the screen. As part of the gamiﬁcation strategy, the game randomly asks
users to pick the word that has not been uttered, rather than the uttered one.
At higher levels of diﬃculty, the phonetic transcription of each word, otherwise
visible, is removed. These strategies aim at the promotion of user adaptation
Finally, in the pronunciation mode, participants are asked to separately read
aloud (and record) both words of each minimal pair. A real-time feedback is
provided instantly. Native model pronunciations of each word can be played as
many times as the user needs. Speech is recorded and played using third party
ASR and TTS applications.
4 TipTopTalk! Mobile application for speech training
TipTopTalk! adapts to the player in function of the interaction results giving a
speciﬁc feedback. New training modes are suggested based on the results of the
current one. For instance, in discrimination mode, if an user achieves the maxi-
mum score, advancement to a pronunciation mode will be suggested. Otherwise,
going back to exposure mode will be automatically recommended after a low
score has been attained in discrimination. Each TipTopTalk! teaching strategy
has its visual user interface containing diﬀerent game elements. Figure 3 shows
three visual user interface screenshots of the main game modes, that is, exposure,
discrimination and pronunciation.
Gamiﬁcation is an informal umbrella term for the use of video game elements
in non-gaming systems to improve user experience (UX) and user engagement.
In TipTopTalk! users add points to their phonetic level and reach several achieve-
ments dependent on the mode and diﬃculty level (see Figure 2 (b)). There are
also diﬀerent language-dependent leaderboards, based on scores attained and the
number of completed rounds, where all players are ranked to increase engage-
ment through competition (see Figure 2 (a)).
Fig. 2. Examples of gamiﬁcation elements: a leaderboard (a) and a list of user’s trophies
Sharing results via social networks plays an important role in the gamiﬁca-
tion strategy by virtue of the competitiveness that it promotes. There are other
gamiﬁcation elements such as a limited time to complete the current round or a
game; the granting of more or less points depending on the diﬃculty level and
the number of attempts required for completion; the allotting of a number of re-
serve lives to allow further playing; the dispensation of an amount of clear tickets
which allow users to skip the current round and move on to next one; and the
TipTopTalk! Mobile application for speech training 5
graphical display of the visual percentage of a game list result. Finally, we incor-
porate a system of push notiﬁcations that sends motivational and challenging
messages to users in order to trigger their engagement.
3 Activities in the demonstration
The demonstration will consist on an interactive session showing all diﬀerent
modes in the client application (see 2.2). People will be able to ask for help
during the presentation. At the beginning, all attending people can download
the application with a given URL or taking a photo of a QR picture. Once
downloaded, the demonstration begins choosing the Spanish language. The ﬁrst
step is to complete an exposure activity, listening to and repeating all words. The
ﬁrst image (a) of Figure 3 shows a basic round of the exposure training mode.
There is a menu-options bar at the top in which users can exit the current game,
go forward to the next round or go back. There is also a status bar below the
menu-options bar that indicates to users the current round. The system allows
us to register whether users play the model for both words at the beginning of
each round. Orthographic forms and phonetic transcriptions are displayed at the
center of the screen. We keep track of the number of times users synthesize a
word or record themselves. We save the recorded voice in a ﬁle for subsequent
analyses and corpus compilation.
The second screenshot (b) of Figure 3 (discrimination mode) includes new
elements such as a timer at the top and both discrimination wrong and correct
counters. There is a background colour as a gamiﬁcation element. If the colour
is green, users must choose the word they think is being played. However, if the
background colour is red, they must choose the wrong one. In the right bottom
corner there is a button that plays another time the sound of the word.
The third screen capture (c) in Figure 3 represents a snapshot of a pronuncia-
tion mode round. This part of the game introduces more feedback elements than
the previous. When the user utters the test word correctly, the related elements
change their base color to green, and the word gets disabled as a positive feedback
message appears. Otherwise, a message appears containing the words recognized
by the ASR (diﬀerent from the test word) together with a non-positive feedback.
The mispronounced word changes its base color to red and remains active before
it gets disabled only after ﬁve unrecognized realizations by the user. There is a
limit of ﬁve wrong attempts per word.
The last screenshot (d) represents a round of Inﬁnite Mode with the variant of
the discrimination mode. The aim of this mode is to complete the highest number
of rounds possible. There are new elements such as number of remaining lives at
the left-top corner, the current round at the top-right corner and a skip-rounf
button at the left-bottom corner. Discrimination and pronunciation challenges
are presented randomly in each round. Users start with a ﬁnite number of lives
that will decrease in one each time they fail. Also, the game’s diﬃculty level
increases with each round. For instance, from the tenth round on, the chance
that the orthographic representation a word is substituted by asterisks is raised
6 TipTopTalk! Mobile application for speech training
Fig. 3. Visual user interface of exposure (a), discrimination (b), pronunciation (c) and
Inﬁnite (discrimination variant) (d) modes.
TipTopTalk! Mobile application for speech training 7
to 50%. From the twentieth round on, a 50% chance that the TTS button is
absent is introduced. The amount of time allotted for round completion is also
4 Conclusions and future work
In this demonstration we presented a serious game implemented by a mobile
application leaning on third party services. The main goal of our system is to
provide a tool for improving L2 pronunciation with gamiﬁcation elements. The
client application was developed for Android version 2.3.3 and using the Eclipse
development environment. On the one hand, it connects to an own web server.
It works under a GNU/Linux operating system gathering data such as log ﬁles,
messages and picture ﬁles. On the other hand, it relies on several Google services,
for instance Google Voice Search, Google Analytics and Google Play Games.
TipTopTalk!’s dependence on both external ASR and TTS systems for as-
sessing speech production may be a long-term problem since they are black-box
systems. We are considering the possibility of using other open source platforms
or creating a new one adapted speciﬁcally.
There are some points that can be improved in future versions. We are now
working on some international collaborations to expand the range of available
languages. We are also working in the portability to other mobile operating sys-
tems. Finally, despite the introduction of gamiﬁcation elements, an habituation
factor leads to a fall in interest and performance after protracted use. This sug-
gests us to be able to incorporate mechanisms to provide real particularized
feedback based on automatically identiﬁed errors.
Acknowledgements. This work was partially funded by the Ministerio de
Econom´ıa y Competitividad y Fondos FEDER – project key: TIN2014-59852-
R Videojuegos Sociales para la Asistencia y Mejora de la Pronunciaci´on de la
Lengua Espa˜nola – and Junta de Castilla y Le´on – pro ject key: VA145U14 Eval-
uaci´on Autom´atica de la Pronunciaci´on del Espa˜nol Como Lengua Extranjera
para Hablantes Japoneses. We would like to thank Andreia Rauber, Anabela
Rato and Junming Yao for their contribution of the minimal pairs lists.
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