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A Serious Mobile Game with Visual Feedback for Training Sibilant Consonants

  • Ivo Anjos
  • Margarida Grilo
  • Mariana Ascensão
  • Isabel Guimarães
  • João Magalhães
  • Sofia Cavaco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10714)

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.

Notes

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.

References

  1. 1.
    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
  2. 2.
    Articulation Station. http://littlebeespeech.com/articulation_station.php. Accessed 5 Jan 2016
  3. 3.
    ARTUR - the ARticulation TUtoR. http://www.speech.kth.se/multimodal/ARTUR/. Accessed 16 Jan 2016
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Sanjit, K., Bhogal, R.T., Speechley, M.: Intensity of aphasia therapy, impact on recovery. Stroke 34(4), 987–993 (2003)CrossRefGoogle Scholar
  6. 6.
    Carvalho, M.I.P., et al.: Interactive game for the training of Portuguese vowels (2012)Google Scholar
  7. 7.
    Denes, G., et al.: Intensive versus regular speech therapy in global aphasia: a controlled study. Aphasiology 10(4), 385–394 (1996)CrossRefGoogle Scholar
  8. 8.
    Falar a Brincar. https://falarabrincar.wordpress.com/. Accessed 16 Jan 2016
  9. 9.
    Freepik. http://www.freepik.com/. Accessed 28 July 2017
  10. 10.
    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)Google Scholar
  11. 11.
    Guimarães, I.: Ciência e Arte da Voz Humana. Escola Superior de Saúde de Alcoitão (2007)Google Scholar
  12. 12.
    Hall, P.K., Jordan, L.S., Robin, D.A.: Developmental apraxia of speech: theory and clinical practice, p. 200. Pro Ed (1993)Google Scholar
  13. 13.
  14. 14.
    Kreimer, S.: Intensive speech and language therapy found to benefit patients with chronic aphasia after stroke. Neurol. Today 17(12), 12–13 (2017)CrossRefGoogle Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    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)CrossRefGoogle Scholar
  18. 18.
    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
  19. 19.
    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)Google Scholar
  20. 20.
    Nwe, T.L., Foo, S.W., De Silva, L.C.: Speech emotion recognition using hidden Markov models. Speech Commun. 41(4), 603–623 (2003)CrossRefGoogle Scholar
  21. 21.
    Parnandi, A., et al.: Development of a remote therapy tool for childhood apraxia of speech. ACM Trans. Access. Comput. (TACCESS) 7(3), 10 (2015)Google Scholar
  22. 22.
    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)CrossRefGoogle Scholar
  23. 23.
    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)Google Scholar
  24. 24.
    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)Google Scholar
  25. 25.
    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)Google Scholar
  26. 26.
  27. 27.
    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)Google Scholar
  28. 28.
    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
  29. 29.
    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
  30. 30.
    VITHEA - Virtual Therapist for Aphasia treatment. https://vithea.l2f.inesc-id.pt/wiki/index.php/Main_Page. Accessed 16 Jan 2016

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.NOVA LINCS, Department of Computer Science, Faculdade de Ciências e TecnologiaUniversidade NOVA de LisboaCaparicaPortugal
  2. 2.Escola Superior de Saúde do AlcoitãoAlcabidechePortugal

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