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A System to Support Children in Speech Therapies at Home

Published: 13 July 2021 Publication History

Abstract

Voice disorders occur when voice quality, pitch, and volume differ or are inadequate for an individual's age, gender, cultural background, or geographic location. These are due to inherent internal and/or external factors, such as vocal cord damage, brain damage, muscle weakness, or vocal cord paralysis that often damage the vocal folds. Commonly the age range of the patients is 4-6 years old. To overcome these problems, speech therapy is needed, which consists of a set of exercises aiming to stimulate the child's language. A personalized treatment for each patient should be defined in accordance with the patient's specific problems. Since speech exercises, even if they usually are proposed as games, are often boring for the children and their caregivers, this research proposed the system Pronuntia, which supports all the actors involved in the speech therapy. The automatic acquisition and correction of the speech exercises through the system allows real-time feedback to patients and therapists. Moreover, some AI techniques have been implemented to help therapists in tuning the automatic recognition level of the patient's speech according to the disease severity. A user test involving 5 speech therapists and 5 caregivers has been carried out to evaluate the usability of the system.

References

[1]
Butcher, P., Elias, A., Raven, R., Yeatman, J. and Littlejohns, D. 1987. Psychogenic voice disorder unresponsive to speech therapy: Psychological characteristics and cognitive-behaviour therapy British Journal of Disorders of Communication 22, 1 (1987/01/01 1987), 81-92.
[2]
Bernthal, J. E., Bankson, N. W. and Flipsen, J. P. 2016. Articulation and phonological disorders: Speech sound disorders in children. Pearson, Boston, MA.
[3]
Furlong, L., Morris, M., Serry, T. and Erickson, S. 2018. Mobile apps for treatment of speech disorders in children: An evidence-based analysis of quality and efficacy PLOS ONE 13, 8 (2018), e0201513.
[4]
Jesus Luis, M. T., Martinez, J., Santos, J., Hall, A. and Joffe, V. 2019. Comparing Traditional and Tablet-Based Intervention for Children With Speech Sound Disorders: A Randomized Controlled Trial Journal of Speech, Language, and Hearing Research 62, 11 (2019/11/22 2019), 4045-4061.
[5]
Pentiuc, S., Tobolcea, I., Schipor, O., Danubianu, M. and Schipor, D. 2010. Translation of the Speech Therapy Programs in the Logomon Assisted Therapy System Advances in Electrical and Computer Engineering 10 (2010), 48-52
[6]
Lopes, M., Magalhães, J. and Cavaco, S. 2016. A voice-controlled serious game for the sustained vowel exercise. In Proceedings of the Proceedings of the 13th International Conference on Advances in Computer Entertainment Technology, 2016, Osaka, Japan, Association for Computing Machinery, Article 32. https//doi.org/10.1145/3001773.3001807
[7]
Nasiri, N., Shirmohammadi, S. and Rashed, A. 2017. A serious game for children with speech disorders and hearing problems. 2017.
[8]
Rossano, V., Roselli, T. and Calvano, G. 2018. A Serious Game to Promote Environmental Attitude. Springer, Cham.
[9]
Cassano, F., Piccinno, A., Roselli, T. and Rossano, V. 2019. Gamification and Learning Analytics to Improve Engagement in University Courses. Springer International Publishing. 2019.
[10]
Ardito, C., Buono, P., Costabile, M. F., Lanzilotti, R. and Pederson, T. 2007. Re-experiencing History in Archaeological Parks by Playing a Mobile Augmented Reality Game. Springer Berlin Heidelberg. 2007.
[11]
Marengo, A., Pagano, A. and Ladisa, L. 2016. Game-Based learning in mobile technology. EUROSIS. 2016.
[12]
Paterno, F. and Wulf, V. 2017. New Perspectives in End-User Development. Springer International Publishing. 2017.
[13]
Costabile, M. F., Fogli, D., Lanzilotti, R., Mussio, P. and Piccinno, A. 2006. Supporting Work Practice Through End-User Development Environments Journal of Organizational and End User Computing 18, 4 (2006), 43-65.
[14]
Costabile, M. F., Fogli, D., Marcante, A., Mussio, P., Parasiliti Provenza, L. and Piccinno, A. 2008. Designing Customized and Tailorable Visual Interactive Systems International Journal of Software Engineering and Knowledge Engineering 18, 3 (2008), 305-325.
[15]
Carmien, S., Dawe, M., Fischer, G., Gorman, A., Kintsch, A. and Sullivan Jr., J. F. 2005. Socio-technical environments supporting people with cognitive disabilities using public transportation ACM T Comput-Hum Int 12, 2 (2005), 233-262.
[16]
Fischer, G., Piccinno, A. and Ye, Y. 2008. The Ecology of Participants in Co-evolving Socio-technical Environments. Springer. 2008.
[17]
Fogli, D. and Piccinno, A. 2013. Co-evolution of End-User Developers and Systems in Multi-tiered Proxy Design Problems. Springer, Berlin Heidelberg. 2013.
[18]
Yujian, L. and Bo, L. 2007. A Normalized Levenshtein Distance Metric IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 6 (2007), 1091-1095.
[19]
Hart, S. G. 2006. Nasa-Task Load Index (NASA-TLX); 20 Years Later Proceedings of the Human Factors and Ergonomics Society Annual Meeting 50, 9 (2006/10/01 2006), 904-908.
[20]
Ardito, C., Desolda, G., Lanzilotti, R., Malizia, A. and Matera, M. 2020. Analysing trade-offs in frameworks for the design of smart environments Behaviour & Information Technology 39, 1 (2020/01/02 2020), 47-71.
[21]
Ardito, C., Buono, P., Desolda, G. and Matera, M. 2018. From smart objects to smart experiences: An end-user development approach INT J HUM-COMPUT ST 114 (2018/06/01/ 2018), 51-68.
[22]
Benzi, F., Cabitza, F., Fogli, D., Lanzilotti, R. and Piccinno, A. 2015. Gamification Techniques for Rule Management in Ambient Intelligence. Springer International Publishing. 2015.
[23]
Ardito, C., Desolda, G., Lanzilotti, R., Malizia, A., Matera, M., Buono, P. and Piccinno, A. 2020. User-defined semantics for the design of IoT systems enabling smart interactive experiences Pers Ubiquit Comput (2020/06/12 2020).

Cited By

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  • (2024)Audio-Only Phonetic Segment Classification Using Embeddings Learned From Audio and Ultrasound Tongue Imaging DataIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2024.347331632(4501-4510)Online publication date: 1-Jan-2024
  • (2024)AI-based automated speech therapy tools for persons with speech sound disorder: a systematic literature reviewSpeech, Language and Hearing10.1080/2050571X.2024.235927428:1Online publication date: 3-Jun-2024
  • (2024)Identifying data elements and key features to design a telerehabilitation system for speech and language disorders in children with hearing impairments in Iran: A cross‐sectional studyHealth Science Reports10.1002/hsr2.700557:9Online publication date: 10-Sep-2024
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cover image ACM Other conferences
CHItaly '21: Proceedings of the 14th Biannual Conference of the Italian SIGCHI Chapter
July 2021
237 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2021

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Author Tags

  1. Speech therapy
  2. remote rehabilitation system
  3. user study
  4. voice disorder

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CHItaly '21

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Overall Acceptance Rate 109 of 242 submissions, 45%

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Cited By

View all
  • (2024)Audio-Only Phonetic Segment Classification Using Embeddings Learned From Audio and Ultrasound Tongue Imaging DataIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2024.347331632(4501-4510)Online publication date: 1-Jan-2024
  • (2024)AI-based automated speech therapy tools for persons with speech sound disorder: a systematic literature reviewSpeech, Language and Hearing10.1080/2050571X.2024.235927428:1Online publication date: 3-Jun-2024
  • (2024)Identifying data elements and key features to design a telerehabilitation system for speech and language disorders in children with hearing impairments in Iran: A cross‐sectional studyHealth Science Reports10.1002/hsr2.700557:9Online publication date: 10-Sep-2024
  • (2023)Human-Centered AI Goals for Speech Therapy ToolsComputer-Human Interaction Research and Applications10.1007/978-3-031-49368-3_8(121-136)Online publication date: 23-Dec-2023
  • (2022)Home-Based Activities for Children with Speech Sound Disorders: Requirements for a Tangible User Interface for Internet of Things ArtefactsApplied Sciences10.3390/app1218897112:18(8971)Online publication date: 7-Sep-2022

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