Development and Evaluation of Remote Articulation Test System to Support Collaboration Between Special Education Classes and External Experts
Abstract
We developed a remote articulation test system with which external expert remotely inspected the speech pronunciation of language-disabled students in special education classes. Our system was configured using Web Real-Time Communication and satisfied the requirements to qualify as a support system for special education classes. To confirm whether a remote articulation test's performance with our system is comparable to a face-to-face test, we compared the performances of the remote test using our system with face-to-face and remote tests using an existing web video system. External experts and teachers conducted articulation tests and rated their diagnostic confidence. The strength of agreement in the diagnostic results was sufficiently high between the face-to-face and remote sessions and between the inspectors. The diagnostic confidence of the remote test with our system was significantly better than the face-to-face and existing web video system sessions. Our system was also evaluated as having higher usability than the existing web video system. Similar results were obtained in related experiments on severe hearing-impaired children, suggesting that the proposed system is also applicable to them. This research achieved a remote articulation test system that provides sufficient performance, privacy and copyright protection, and ease of use for inspectors.
References
Ministry of Education, Culture, Sports, Science and Technology-Japan, “Survey result of the instruction implementation status in special education classes,” white paper, Mar. 2020; www.mext.go.jp/content/20200317-mxt_tokubetu01-000005538-02.pdf (in Japanese)
Ministry of Education, Culture, Sports, Science and Technology-Japan, Ordinance No. 27, Aug. 2018; www.mext.go.jp/component/a_menu/education/micro_detail/__icsFiles/afieldfile/2018/10/11/1409653_02.pdf (in Japanese)
Z. Rossetti and W. Blackwell, “The development of individualized education programs: Where have we been and where should we go now?” SAGE Open, 4 (2), 2014, pp. 1–15.
Ministry of Education, Culture, Sports, Science and Technology-Japan, “Results of a special support education system development status survey,” white paper, June 2018; www.mext.go.jp/a_menu/shotou/tokubetu/__icsFiles/afieldfile/2018/06/25/1402845_02.pdf (in Japanese)
Ministry of Education, Culture, Sports, Science and Technology-Japan, “Current status and problems of teachers' expertise in special needs education,” white paper, Mar. 2010; https://www.mext.go.jp/b_menu/shingi/chukyo/chukyo3/044/attach/1298226.htm (in Japanese)
Ministry of Education, Culture, Sports, Science and Technology-Japan, “Future special needs education (Final Report),” white paper, Mar. 2003; https://www.mext.go.jp/b_menu/shingi/chousa/shotou/054/shiryo/attach/1361204.htm (in Japanese)
R. A. Matthews, B. Woll, and M. Clarke, “Researching the acceptability of using Skype to provide Speech and Language Therapy,” International Journal of Integrated Care, 12, 2012, e114.
J. Ashburner, S. Vickerstaff, and J. Copley, “Remote versus face-to-face delivery of early intervention programs for children with autism spectrum disorders: Perceptions of rural families and service providers,” Research in Autism Spectrum Disorders, 23, 2016, pp. 1–14.
S. Dudy, S. Bedrick, M. Asgari, and A. Kain, “Automatic analysis of pronunciations for children with speech sound disorders,” Computer Speech and Language, Vol. 50, 2018, pp. 62–84.
M. Russell, R. W. Series, J. L. Wallace, C. Brown, and A. Skilling, “The STAR system: an interactive pronunciation tutor for young children,” Computer Speech and Language, Vol. 14, 2000, pp. 161–175.
Y. Chen and J. Wu, “Development of articulation assessment and training system with speech recognition and articulation training strategies selection,” Proc. ICASSP, 2007, pp. IV-209–IV-212.
R. Vijayalakshmi and S. Priya, “An interactive speech therapy session using linear predictive coding in Matlab and Arduino,” Proc. ICACCCT, 2016, pp. 217–220.
H. Su, C. Wu, and P. Tsai, “Automatic assessment of articulation disorders using confident unit-based model adaptation,” Proc. ICASSP, 2008, pp. 4513–4516.
Y. Chen, J. Wu, H. Yang, C. Wu, C. Chen, and S. Ju, “An articulation training system with intelligent interface and multimode feedbacks to articulation disorders,” Proc. International Conference on Asian Language, 2009, pp. 3–6.
S. Ng and T. Lee, “Automatic detection of phonological errors in child speech using Siamese recurrent autoencoder,” Proc. INTERSPEECH, 2020, pp. 4476–4480.
I. Masuda-Katsuse, “Web application system for pronunciation practice by children with disabilities and to support cooperation of teachers and medical workers,” Proc. INTERSPEECH, 2015, pp.1066–1067.
A. P. Vidul, S. Hari, K. P. Pranave, K. J. Vysakh, and K. R. Archana, “Telemedicine for Emergency Care Management using WebRTC,” Proc. International Conference on Advances in Computing, Communications and Informatics, 2015, pp. 1741–1745.
A. B. Jhonstron and D. C. Burnett, WebRTC: APIs and RTCWEB Protocols of the HTML5 Real-time Web, Digital Codex LLC, 2013.
Articulation Clinical Study Group, Articulation test –New Edition, Chiba Test Center, Tokyo, 2010. (in Japanese)
K. Gwet, “Inter-rater reliability: Dependency on trait prevalence and marginal homogeneity,” Statistical Methods for Inter-Rater Reliability Assessment, No. 2, 2002, pp. 1–9.
J. R. Landis and G. G. Koch, “The measurement of observer agreement for categorical data,” Biometrics, 33 No. 1, 1977, pp. 159–174.
B. Anthony Jnr., “Use of Telemedicine and Virtual Care for Remote Treatment in Response to COVID-19 Pandemic,” Journal of Medical Systems, 44: 132, 2020, pp. 1–9.
