Students with physical disabilities often have difficulty with writing fluency, despite the use of various strategies, adaptations, and assistive technology (AT). One possible intervention is the use of speech recognition software, although there is little research on its impact on students with physical disabilities. This study used an alternating treatments design to compare the use of speech recognition software to word processing on first draft writing with students with physical disabilities. Areas examined were fluency, accuracy, type of word errors, recall of intended meaning, and length. The results of this study indicated that fluency and length were greater for all participants with speech recognition compared to word processing, but accuracy was lower using speech recognition.
Get full access to this article
View all access options for this article.
References
1.
BarlowD. H.HayesS. C. (1979). Alternating-treatments design: One strategy for comparing the effects of two treatments in a single subject. Journal of Applied Behavior Analysis, 12, 199–210.
2.
BestS.HellerK. W.BiggeJ. (2010). Teaching individuals with physical, health, or multiple disabilities (6th ed.). Upper Saddle River, NJ: Merrill/Prentice Hall.
3.
BruceC.EdmundsonA.ColemanM. (2003). Writing with voice: An investigation of the use of a speech recognition system as a writing aid for a man with aphasia. International Journal of Language & Communication Disorders, 38(2), 131–148.
4.
De La PazS. (1999). Composing via dictation and speech recognition systems: Compensatory technology for students with learning disabilities. Learning Disability Quarterly, 22, 173–182.
5.
FengJ.KaratC. M.SearsA. (2005). How productivity improves in hands-free continuous dictation tasks: Lessons learned from a longitudinal study. Interacting with Computers, 17, 265–289.
6.
HellerK. W. (2009). Learning and behavioral characteristics of students with physical, health, or multiple disabilities. In HellerK.W.ForneyP.E.AlbertoP.A.BestS.SchwartzmanM. N. (Eds.), Understanding physical, health, and multiple disabilities, (2nd ed., pp. 35–50). Upper Saddle River, NJ: Merrill/Prentice Hall.
7.
HellerK. W. (2010). Writing instruction and adaptations. In BestS.HellerK. W.BiggeJ. (Eds.), Teaching individuals with physical, health, or multiple disabilities, (6th ed., pp. 407–431). Upper Saddle River, NJ: Merrill/Prentice Hall.
8.
HoneycuttL. (2003). Researching the use of speech recognition writing software. Computers and Composition, 20, 77–95.
9.
HuxK.Rankin-EricksonJ.ManasseN.LauritzenE. (2000). Accuracy of three speech recognition systems: Case study of dysarthric speech. AAC Augmentative and Alternative Communication, 16, 186–196.
10.
KaratJ.HornD. B.HalversonC. A.KaratC. M. (2000, April). Overcoming unusability: Developing efficient strategies in speech recognition systems. Poster session presented at CHI 2000, the AMC Conference on Human Factors in Computer Systems, The Hague, Netherlands.
11.
KelloggR. T. (1996). A model of working memory in writing. In LevyC. M.RansdellS. (Eds.), The science of writing: Theories, methods, individual differences, and applications (pp. 57–71). Mahwah, NJ: Erlbaum.
12.
KoesterH. H. (2001). User performance with speech recognition: A literature review. Assistive Technology, 13, 116–130.
13.
KoesterH. H. (2003). Abandonment of speech recognition by new users. Paper presented at the Rehabilitation Engineering and Assistive Technology Society of North America 26th International Annual Conference, Atlanta, GA.
14.
KoesterH. H. (2004). Usage, performance, and satisfaction outcomes for experienced users of automatic speech recognition. Journal of Rehabilitation Research & Development, 41, 739–754.
15.
KotlerA. L.TamC. (2002). Effectiveness of using discrete utterance speech recognition software. AAC Augmentative and Alternative Communication, 18, 137–146.
16.
KotlerA. L.Thomas-StonellN. (1997). Effects of speech training on the accuracy of speech recognition for an individual with a speech impairment. AAC Augmentative and Alternative Communication, 13, 71–80.
17.
LewisR. B.GravesA. W.AshtonT. M.KieleyC. L. (1998). Word processing tools for students with learning disabilities: A comparison of strategies to increase text entry speed. Learning Disabilities Research & Practice, 13, 95–108.
18.
MacArthurC. A. (2000). New tools for writing: Assistive technology for students with writing difficulties. Topics in Language Disorders, 20(4), 85–100.
19.
MacArthurC. A.CavalierA. R. (2004). Dictation and speech recognition technology as test accommodations. Exceptional Children, 71, 43–58.
20.
Peterson-KarlanG.HourcadeJ. J.ParetteP. (2008). A review of assistive technology and writing skills for students with physical and educational disabilities. Physical Disabilities: Education and Related Services, 26(2), 13–32.
21.
QuinlanT. (2004). Speech recognition technology and students with writing difficulties: Improving fluency. Journal of Educational Psychology, 96, 337–346.
22.
SandbergA. D. (2001). Reading and spelling, phonological awareness, and working memory in children with severe speech impairments: A longitudinal study. AAC Augmentative and Alternative Communication, 17, 11–26.
23.
ShaerC. (1997). The infant and young child with Spina Bifida: Major medical concerns. Infants and Young Children, 9(3), 13–25.
24.
TumlinJ.HellerK. W. (2004). Using word prediction software to increase typing fluency with students with physical disabilities. Journal of Special Education Technology, 19 (3), 5–14.
25.
WilkinsonG. S. (1993). Wide Range Achievement Test 3. Wilmington, DE: Wide Range.
