Abstract
The distortion of sibilant sounds is a common type of speech sound disorder (SSD) in Portuguese speaking children. Speech and language pathologists (SLP) frequently use the isolated sibilants exercise to assess and treat this type of speech errors.
While technological solutions like serious games can help SLPs to motivate the children on doing the exercises repeatedly, there is a lack of such games for this specific exercise. Another important aspect is that given the usual small number of therapy sessions per week, children are not improving at their maximum rate, which is only achieved by more intensive therapy.
We propose a serious game for mobile platforms that allows children to practice their isolated sibilants exercises at home to correct sibilant distortions. This will allow children to practice their exercises more frequently, which can lead to faster improvements. The game, which uses an automatic speech recognition (ASR) system to classify the child sibilant productions, is controlled by the child’s voice in real time and gives immediate visual feedback to the child about her sibilant productions.
In order to keep the computation on the mobile platform as simple as possible, the game has a client-server architecture, in which the external server runs the ASR system. We trained it using raw Mel frequency cepstral coefficients, and we achieved very good results with an accuracy test score of above \(91\%\) using support vector machines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
American Speech-Language-Hearing Association (ASHA) - Speech Sound Disorders: Articulation and Phonological Processes. http://www.asha.org/public/speech/disorders/SpeechSoundDisorders/. Accessed 28 July 2017
Articulation Station. http://littlebeespeech.com/articulation_station.php. Accessed 5 Jan 2016
ARTUR - the ARticulation TUtoR. http://www.speech.kth.se/multimodal/ARTUR/. Accessed 16 Jan 2016
Barratt, J., Littlejohns, P., Thompson, J.: Trial of intensive compared with weekly speech therapy in preschool children. Arch. Dis. Child. 67(1), 106–108 (1992)
Sanjit, K., Bhogal, R.T., Speechley, M.: Intensity of aphasia therapy, impact on recovery. Stroke 34(4), 987–993 (2003)
Carvalho, M.I.P., et al.: Interactive game for the training of Portuguese vowels (2012)
Denes, G., et al.: Intensive versus regular speech therapy in global aphasia: a controlled study. Aphasiology 10(4), 385–394 (1996)
Falar a Brincar. https://falarabrincar.wordpress.com/. Accessed 16 Jan 2016
Freepik. http://www.freepik.com/. Accessed 28 July 2017
Ganapathy, S., Thomas, S., Hermansky, H.: Comparison of modulation features for phoneme recognition. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 5038–5041. IEEE (2010)
Guimarães, I.: Ciência e Arte da Voz Humana. Escola Superior de Saúde de Alcoitão (2007)
Hall, P.K., Jordan, L.S., Robin, D.A.: Developmental apraxia of speech: theory and clinical practice, p. 200. Pro Ed (1993)
Image Quiz: Vocal Tract. http://www.imagequiz.co.uk/img?img_id=ag5zfmltYWdlbGVhcm5lcnIQCxIHUXVpenplcxifotErDA. Accessed 30 July 2017
Kreimer, S.: Intensive speech and language therapy found to benefit patients with chronic aphasia after stroke. Neurol. Today 17(12), 12–13 (2017)
Lan, T., et al.: Flappy voice: an interactive game for childhood apraxia of speech therapy. In: Proceedings of the First ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play, pp. 429–430. ACM (2014)
Lopes, M., Magalhães, J., Cavaco, S.: A voice-controlled serious game for the sustained vowel exercise. In: Proceedings of the 13th International Conference on Advances in Computer Entertainment Technology, p. 32. ACM (2016)
Matejka, P., Schwarz, P., et al.: Analysis of feature extraction and channel compensation in a GMM speaker recognition system. IEEE Trans. Audio Speech Lang. Process. 15(7), 1979–1986 (2007)
Mobile Operating System Market Share in Portugal, 2016 to 2017. http://gs.statcounter.com/os-market-share/mobile/portugal/#yearly-2016-2017-bar. Accessed 25 July 2017
Nefian, A.V., et al.: A coupled HMM for audio-visual speech recognition. In: 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 2, pp. II–2013. IEEE (2002)
Nwe, T.L., Foo, S.W., De Silva, L.C.: Speech emotion recognition using hidden Markov models. Speech Commun. 41(4), 603–623 (2003)
Parnandi, A., et al.: Development of a remote therapy tool for childhood apraxia of speech. ACM Trans. Access. Comput. (TACCESS) 7(3), 10 (2015)
Preston, J., Edwards, M.L.: Phonological awareness and types of sound errors in preschoolers with speech sound disorders. J. Speech Lang. Hear. Res. 53(1), 44–60 (2010)
Rubin, Z., Kurniawan, S.: Speech adventure: using speech recognition for cleft speech therapy. In: Proceedings of the 6th International Conference on Pervasive Technologies Related to Assistive Environments, p. 35. ACM (2013)
Sharma, S., et al.: Feature extraction using non-linear transformation for robust speech recognition on the Aurora database. In: Proceedings of the 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000, vol. 2, pp. II1117–II1120. IEEE (2000)
Shriberg, L.D., Paul, R., Flipsen, P.: Childhood speech sound disorders: from postbehaviorism to the postgenomic era. In: Speech Sound Disorders in Children, pp. 1–33 (2009)
Talker. http://speech-trainer.com/children-speech-therapy. Accessed 15 Jan 2016
Tan, C.T., et al.: sPeAK-MAN: towards popular gameplay for speech therapy. In: Proceedings of the 9th Australasian Conference on Interactive Entertainment: Matters of Life and Death, p. 28. ACM (2013)
The Statistics Portal - Forecast of tablet user numbers in Portugal from 2014 to 2021 (in million users). https://www.statista.com/statistics/566416/predicted-number-of-tablet-users-portugal/. Accessed 02 Feb 2016
The Statistics Portal - Forecast of the tablet user penetration rate in Portugal from 2014 to 2021. https://www.statista.com/statistics/568594/predicted-tablet-user-penetration-rate-in-portugal/. Accessed 02 Feb 2016
VITHEA - Virtual Therapist for Aphasia treatment. https://vithea.l2f.inesc-id.pt/wiki/index.php/Main_Page. Accessed 16 Jan 2016
Acknowledgments
This work was supported by the Portuguese Foundation for Science and Technology under projects BioVisualSpeech (CMUP-ERI/TIC/0033/2014) and NOVA-LINCS (PEest/UID/CEC/04516/2013).
We thank the SLPs Diana Lança and Catarina Duarte for their availability and feedback. We also thank all the 3rd and 4th year SLP students from Escola Superior de Saúde do Alcoitão who collaborated in the data collection task. Many thanks also to Inês Jorge for the graphic design of the game scenarios. Finnally, we would like to thank the schools from Agrupamento de Escolas de Almeida Garrett, and all the children who participated in the recordings.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Anjos, I., Grilo, M., Ascensão, M., Guimarães, I., Magalhães, J., Cavaco, S. (2018). A Serious Mobile Game with Visual Feedback for Training Sibilant Consonants. In: Cheok, A., Inami, M., Romão, T. (eds) Advances in Computer Entertainment Technology. ACE 2017. Lecture Notes in Computer Science(), vol 10714. Springer, Cham. https://doi.org/10.1007/978-3-319-76270-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-76270-8_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76269-2
Online ISBN: 978-3-319-76270-8
eBook Packages: Computer ScienceComputer Science (R0)