Model of a data mining system for personalized therapy of speech disorders
|Published in:||Issue 2, (Vol. 3) / 2009|
|Author(s):||Danubianu Mirela, Pentiuc Stefan Gheorghe, Tobolcea Iolanda, Socaciu Tiberiu|
|Abstract.||Lately, the children with speech disorder have more and more become object of specialists' attention and investment in speech disorder therapy are increasing The development and use of information technology in order to assist and follow speech disorder therapy allowed researchers to collect a considerable volume of data. The aim of this paper is to present a data mining system designed to be associated with TERAPERS system in order to provide information based on which one could improve the process of personalized therapy of speech disorders.|
|Keywords:||Data Mining, Classification, Association Rules, Speech Disorders, Personalized Therapy|
1. Forsberg M., Why is Speech RecognitionDifficult?, Chalmers University of Technology, Göteborg 2003
2. BA Lewis, LA Freebairn, AJ Hansen, CM Stein, LD Shriberg, SK Iyengar, H Gerry Taylor -Dimensions of early speech sound disorders: A factor analytic study, Journal of communication disorders, Vol. 39, No. 2. (r 2006), pp. 139-157
3. Adams, Russ, Sourcebook of Automatic Identification and Data Collection, Van Nostrand Reinhold, New York, 1990
4. Yannakoudakis, E. J., and P. J. Hutton, Speech Synthesis and Recognition Systems, Ellis Horwood Limited, Chichester, UK, 1987
5. Sarma, M.; Mammone, R. Automatic speech segmentation using neural tree networks Neural Networks for Signal Processing 1995. V. Proceedings of the 1995 IEEE Workshop Volume , Issue , 31 Aug-2 Sep 1995 Page(s):282 – 290
6. Jurafsky, D., & Martin, J. H. (2000). Speech and natural language processing. Upper Saddle River, NJ: Prentice Hall.
7. Masahide Sugiyama, Jinichi Murakami, Hideyuki Watanabe Speech segmentation and clustering problem based on an unknown-multiple N signal source model - an application to segmented speech clustering based on speaker features, Systems and Computers in Japan, Vol. 25, Issue 9, P. 83-92
8. OLP (Ortho-Logo-Paedia) – Project for Speech Therapy (http://www.xanthi.ilsp.gr/olp);W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.
9. Diagnostic evaluation of Articulation and Phonology (http://www.harcourtuk.com – May 28, 2008) ;
10.STAR Speech Training, Assessment, and Remediation (http://www.asel.udel.edu/speech - January, 2009);
11.Speechviewer III (http://www.synapseadaptive. com/edmark/prod/sv3 - March, 2009); 12..http://www.ingenix.com/Products/Payers/CareHealthManagem entPAY/EnterpriseWideDecisionSupport/ - April, 2009
13.http://magix.fri.uni-lj.si/idadm/ - March, 2009
14.A.G. Di Nuovo, V. Catania, S. Di Nuovo, S. BuonoPsychology with soft computing: An integrated approach and its applications. Applied Soft Computing, Volume 8, Issue 1, January 2008, p. 829-837
15.Chun-Lang Chang A study of applying data mining to early intervention for developmentally-delayed children, Expert Systems with Applications, Vol. 33, Issue 2, 2007, pag. 407- 412
16.Wirth, R. and Hipp, ( 2000) J. CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, pages 29-39, Manchester, UK.
17.Danubianu M. (2006) Using data mining techniques for decision support systems –Proceedings of the International Conference on Signal/Image Processing and Pattern Recognition -"UkrObraz-2006",pag. 19-22, August 28-31, 2006, Kiev, Ucraina.
18.M. Danubianu, T. Socaciu Does Data Mining Techniques Optimize the Personalized Therapy of Speech Disorders? Journal of Applied Computer Science and Mathematics, ISSN:1843-1046, 2009, pag 15-19
19.Danubianu M. , Pentiuc S.G., Schipor O., Nestor M., Ungurean I., (2008)Distributed Intelligent System for Personalized Therapy of Speech Disorders, Proceedings of ICCGI08, Atena
20.M. Danubianu, S. Gheorghe. PentiucC, Tiberiu Socaciu (2009), Towards the Optimized Personalized Therapy of Speech Disorders by Data Mining Techniques, The Fourth International Multi Conference on Computing in the Global Information Technology ICCGI 2009, Vol: CD, 23-29 August, 2009, Cannes, France, ISSB/ISBN: 978-0-7695-3751-1, Pagini: 1-6
21.H. Peng F. Long, C. Ding - Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, August 2005, pag. 1226-1238
|Back to the journal content|
This article is licensed under a
Creative Commons Attribution-ShareAlike 4.0 International License.