Computers can be used e¤ectively in the assessment of children’s speech and language. Biofeedback instru-mentation allows the clinician to obtain relatively objec-tive measures of certain aspects of speech production.
For example, measures of jitter and shimmer can be recorded, along with perceptual judgments about a cli-ent’s pitch and intensity perturbations (Case, 1999).
Acoustic analyses (Kent and Read, 1992) can be used to supplement the clinician’s perceptions of phonological contrasts (Masterson, Long, and Buder, 1998). For the evaluation of a client suspected of having a fluency dis-order, recent software developments allow the clinician to gather measures of both the number and type of speech disfluencies and to document signs of e¤ort, struggle, or disruption of airflow and phonation (Bak-ker, 1999a). Hallowell (1999) discusses the use of instru-mentation for detecting and measuring eye movements for the purpose of comprehension assessment. This exciting tool allows the clinician to evaluate
comprehen-sion in a client for whom traditional response modes, such as speaking or even pointing, are not possible.
Computers can also be used to administer or score a formal test (Cochran and Masterson, 1995; Hallowell and Katz, 1999; Long, 1999). Computer-based scoring systems allow the input of raw scores, which are then converted to profiles or derived scores of interest (Long, 1999). The value of such programs is inversely related to the ease of obtaining the derived scores by hand. If the translation of raw scores to derived scores is tedious and time-consuming, clinicians might find the software tools worth their investment in time and money.
Although few computerized tests are currently avail-able, the potential for such instruments is quite high.
Hallowell and Katz (1999) point out that computerized test administration could allow tighter standardization of administration conditions and procedures, tracking of response latency, and automated interfacing with alter-native response mode systems. Of particular promise are the computerized tests that adapt to a specific client’s profile. That is, stimuli are presented in a manner that is contingent on the individual’s prior responses (Letz, Green, and Woodard, 1996). The type of task or specific items that are administered can be automatically deter-mined by a client’s ongoing performance (e.g., Master-son and Bernhardt, 2001), which makes individualized assessment more feasible than ever. Incorporation of some principles from artificial intelligence also makes the future of computers in assessment exciting. For example, Masterson, Apel, and Wasowicz (2001) developed a tool for spelling assessment that employs complex algorithms for parsing spelling words into target orthographic structures and then aligning a student’s spelling with the appropriate correct forms. Based on the type of mis-spellings exhibited by each individual student, the sys-tem identifies related skills that need testing, such as phonological awareness or morphological knowledge.
This system makes possible a comprehensive description of a student’s spelling abilities that would otherwise be prohibitive because of the time required to perform the analyses by hand and administer the individualized follow-ups.
Computerized language and phonological sample analysis (CL/PSA) has been in use since the 1980s (Evans and Miller, 1999; Long, 1999; Masterson and Oller, 1999; Long and Channell, 2001). These programs allow researchers and clinicians to perform complex, in-depth analyses that would likely be impossible without the technology. They provide instant analysis of a wide range of phonological and linguistic measures, and some provide tools that reduce and simplify the time-consum-ing process of transcribtime-consum-ing samples (Long, 1999). Many of the CL/PSA programs also include comparison data-bases of language samples from both typical and clinical populations (Evans and Miller, 1999). Despite the power of CL/PSA programs, their use in clinical settings re-mains limited, for unclear reasons. It is possible that funding for software and hardware is insu‰cient; how-ever, data from recent surveys (McRay and Fitch, 1996;
ASHA, 1997) do not support this conjecture, since most
respondents do report owning and using computers for other purposes. Lack of use is more likely related to in-su‰cient familiarity with many of the measures derived from language sample analysis and failure to recognize the benefits of these measures for treatment planning (Cochran and Masterson, 1995; Fitch and McRay, 1997). In an e¤ort to address this problem, Long estab-lished the Computerized Profiling Website (http://
www.computerizedprofiling.org) in 1999. Clinicians can visit the web site and obtain free versions of this CL/
PSA software as well as instructional materials regarding its use and application.
Computer software for use in speech and language intervention has progressed significantly from the early versions, which were based primarily on a drill-and-practice format. Cochran and Nelson (1999) cite litera-ture that confirms what many clinicians knew intuitively:
software that allows the child to be in control and to independently explore based on personal interests is more beneficial than computer programs based on the drill-and-practice model. Improvements in multimedia capacities and an appreciation for maximally e¤ective designs have resulted in a proliferation of software packages that can be e¤ectively used in language inter-vention with young children. As with any tool, the focus must remain on the target linguistic structures rather than the toys or activities that are used to elicit or model productions. In addition to therapeutic benefits, com-puters o¤er reasonable compensatory strategies for older, school-age students with language-learning dis-abilities (Wood and Masterson, 1999; Masterson, Apel, and Wood, 2002). For example, word processors with text-to-speech capabilities allow students to check their own work by listening to as well as reading their text.
Spell and grammar checkers can be helpful, as long as students have been su‰ciently trained in the optimal use of these tools, including an appreciation of their limita-tions. Speech recognition systems continue to improve, and perhaps someday they will free writers with lan-guage disorders from the burden of text entry, which requires choices regarding spelling, and spelling can be so challenging for students with language disorders that it interferes with text construction. Currently, speech recognition technology remains limited in recognition accuracy for students with language disorders (Wetzel, 1996). Even when accuracy improves to an acceptable level, students will still need specific training in the opti-mal use of the technology. Optiopti-mal writing involves more than a simple, direct translation of spoken lan-guage to written form. Students who employ speech rec-ognition software to construct written texts will need focused instruction regarding the di¤erences between the styles of spoken and written language. Finally, the Internet provides not only a context for language inter-vention, but a potential source of motivation as well.
The percentage of school-age children who use the Internet on a daily basis for social as well as academic purposes continues to increase, and it is likely that speech-language pathologists will capitalize on this trend.
Computers add a new twist to an old standard in phonological treatment. Instead of having to sort and carry numerous picture cards from one treatment ses-sion to the next, clinicians can choose one of several software packages that allow access and display of multimedia stimuli on the basis of phonological charac-teristics (Masterson and Rvachew, 1999). New tech-nologies, such as the palatometer, provide clients with critical feedback for sound production when tactile or kinesthetic feedback has not been su‰cient. Similarly, computer programs can be used to provide objective feedback regarding the frequency of stutterings, which might be considered less confrontational than feedback provided by the clinician (Bakker, 1999b). One particu-larly promising technology, the Speech Enhancer, incor-porates real-time processing of an individual’s speech production and selectively boosts energy only in those frequencies necessary for maximum intelligibility. Car-iski and Rosenbek (1999) collected data from a single subject and found that intelligibility scores were higher when using the Speech Enhancer than when using a high-fidelity amplifier. The authors suggested that their results supported the notion that the device did indeed do more than simply amplify the speech output.
The decision to use computers in both assessment and treatment activities will continue to be based on the clinician’s judgment as to the added value of the tech-nology application. If a clinician can do an activity just as well without a computer, it is unlikely that she or he will go to the expense in terms of time and money to in-vest in the computer tool. On the other hand, for those tasks that cannot be done as well or even at all, clinicians will likely turn to the computer if they are convinced that the tasks themselves are worth it.
See also aphasia treatment: computer-aided rehabilitation.
—Julie J. Masterson
References
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Long, S. H., and Channell, R. W. (2001). Accuracy of four language analysis procedures performed automatically.
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Masterson, J., Apel, K., and Wasowicz, J. (in press). Spelling Evaluation for Language and Literacy (SPELL) [computer software]. Evanston, IL: Learning by Design.
Masterson, J., Apel, K., and Wood, L. (2002). Linking soft-ware and hardsoft-ware applications to what we know about literacy development. In K. Butler and E. Silliman (Eds.), Speaking, reading, and writing in children with language-learning disabilities: New paradigms for research and prac-tice (pp. 273–293). Mahwah, NJ: Erlbaum.
Masterson, J., and Bernhardt, B. (2001). Computerized Ar-ticulation and Phonology Evaluation System (CAPES) [computer software]. San Antonio, TX: Psychological Cor-poration.
Masterson, J., Long, S., and Buder, E. (1998). Instrumentation in clinical phonology. In J. Bernthal and N. Bankson (Eds.), Articulation and phonological disorders (4th ed., pp. 378–406). Englewood Cli¤s, NJ: Prentice-Hall.
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Wood, L. A., and Masterson, J. J. (1999). The use of technol-ogy to facilitate language skills in school-age children.
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Further Readings
American Guidance Service. (1997). PPVT-III ASSIST [Com-puter software]. Circle Pines, MN: Author.
Case, J. L. (1999). Technology in the treatment of voice dis-orders. Seminars in Speech and Language, 20, 281–295.
Farrall, J. L., and Parsons, C. L. (1992). A comparison of a traditional test format vs. a computerized administration of the Carrow-Woolfolk Test for Auditory Comprehension of Language. Australian Journal of Human Communication Disorders, 20, 33–48.
Fitch, J. L., and McRay, L. B. (1997). Integrating technology into school programs. Language, Speech, and Hearing Services in Schools, 28, 134–136.
Friel-Patti, S., DesBarres, K., and Thibodeau, L. (2001). Case studies of children using Fast ForWord. Journal of Speech-Language Pathology, 10, 203–215.
Jamieson, D. G., and Rvachew, S. (1992). Remediation of speech production errors with sound identification training.
Journal of Speech-Language Pathology and Audiology, 16, 201–210.
Katz, R. C., and Hallowell, B. (1999). Technological applica-tions in the treatment of acquired neurogenic communica-tion and swallowing disorders in adults. Seminars in Speech and Language, 20, 251–269.
Long, S. H., and Channell, R. W. (2001). Accuracy of four language analysis procedures performed automatically.
American Journal of Speech-Language Pathology, 10, 180–
188.
Long, S. H., Fey, M. E., and Channell, R. W. (1998). Com-puterized Profiling (CP) (Version 9.0) (MS-DOS) [Computer program]. Cleveland, OH: Department of Com-munication Sciences, Case Western Reserve University.
MacWhinney, B. (1998). The CHILDES project: Computa-tional tools for analyzing talk. Mahwah, NJ: Erlbaum.
Masterson, J., and Crede, L. (1999). Learning to spell: Impli-cations for assessment and intervention. Language, Speech, and Hearing Services in Schools, 30, 243–254.
Masterson, J., and Perrey, C. (1999). Training analogical rea-soning skills in children with language disorders. American Journal of Speech-Language Pathology, 8, 53–61.
Masterson, J., Wynne, M., Kuster, J., and Stierwalt, J. (1999).
New and emerging technologies: Going where we’ve never gone before. ASHA, 41, 16–20.
Matesich, J., Porch, B., and Katz, R. (1996). PICApad PC [Computer software]. Scottsdale, AZ: Sunset Software.
Miller, J., Freiberg, C., Rolland, M.-B., and Reeves, M.
(1992). Implementing computerized language sample anal-ysis in the public school. C. Dollaghan (Ed.), Topics in lan-guage disorders. Rockville, MD: Aspen Press.
Miller, J. F., and Chapman, R. S. (1998). Systematic analysis of language transcripts (SALT) (Version 4.0, MS-DOS) [Computer program]. Madison, WI: Language Analysis Laboratory, Waisman Center on Mental Retardation and Human Development, University of Wisconsin.
Nelson, L., and Masterson, J. (1999). Using microcomputer technology to advance assessment and intervention for children with language disorders. Topics in Language Dis-orders, 19, 68–86.
Rvachew, S. (1994). Speech perception training can facilitate sound production learning. Journal of Speech and Hearing Research, 37, 347–357.
Tye-Murray, N. (1992). Laser videodisc technology in the aural rehabilitation setting: Good news for people with se-vere and profound hearing impairments. American Journal of Audiology, 1, 33–36.
Wilkinson, G. S. (1993). Wide Range Achievement Test 3 (WRAT3) Scoring Program [Computer software]. Odessa, FL: Psychological Assessment Resources.
Woodcock, R. W. (1998). Woodcock Scoring and Interpretive Program [Computer software]. Itasca, IL: Riverside.