Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/10231
Title: | Speakmore: A mobile app towards better stuttering awareness to improve speech therapy |
Authors: | Dr. YUEN Man-Ching, Connie Chu, Shin Ying Din, Normah Binti Che |
Issue Date: | 2018 |
Publisher: | Place of publication not identified : CSREA Press |
Source: | Yuen, M. C., Chu, S. Y., & Din, N. B. C. (2018). Speakmore: A mobile app towards better stuttering awareness to improve speech therapy. In Arabnia, H. R., Leonidas Deligiannidis, F. G. T., & Tran, Q. (Eds.). HIMS' 18 : Proceedings of the 2018 international conference on health informatics and medical systems. 2018 international conference on health informatics and medical systems, USA (pp. 66-69). Place of publication not identified : CSREA Press. |
Conference: | 2018 international conference on health informatics and medical systems |
Abstract: | Generalization of treatment is a challenging task for speech therapists and adult who stutter (AWS), and it can contribute a barrier to long-term treatment success. Given that AWS often are aware of their speech problems at different speaking situations, self-evaluation of their own speech performance in real-life communication situations could be used to control their stuttering. This project aims to develop a mobile application for smartphones, 6SHDN0RUH , directed to AWS and speech therapist usage. This software will allow the AWS to register their stuttering related situations, selfevaluate their anxiety level and fluency level. It could help to promote daily self-monitoring of speech as a means of controlling stuttering. This system also allows speech therapists to monitor, receive report, and score their client’s speech performance. This collected data could use to improve the therapeutic process using their client’s personalized reallife communication situations. |
Type: | Conference Paper |
URI: | https://ebookcentral-proquest-com.hksyu.idm.oclc.org/lib/syu/reader.action?docID=5742568 http://hdl.handle.net/20.500.11861/10231 |
ISBN: | 9781601324795 9781683924364 |
Appears in Collections: | Applied Data Science - Publication |
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