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Journal articles on the topic 'Speech generating devices'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Doyle, Molly, and Wendy Burton. "Update on Medicare and Speech Generating Devices." Perspectives on Augmentative and Alternative Communication 14, no. 1 (April 2005): 13–17. http://dx.doi.org/10.1044/aac14.1.13.

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2

BLISCHAK, DOREEN, LINDA LOMBARDINO, and ALICE DYSON. "Use of Speech-Generating Devices: In Support of Natural Speech." Augmentative and Alternative Communication 19, no. 1 (January 2003): 29–35. http://dx.doi.org/10.1080/0743461032000056478.

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3

Kander, Mark. "Will Payers Cover Speech-Generating Apps?" ASHA Leader 18, no. 1 (January 2013): 24–25. http://dx.doi.org/10.1044/leader.bml.18012013.24.

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4

Shepherd, Tracy A., Kent A. Campbell, Anne Marie Renzoni, and Nahum Sloan. "Reliability of Speech Generating Devices: A 5-Year Review." Augmentative and Alternative Communication 25, no. 3 (January 2009): 145–53. http://dx.doi.org/10.1080/07434610902996104.

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5

Ballin, Liora, Susan Balandin, Roger J. Stancliffe, and Leanne Togher. "Speech-language pathologists' views on mentoring by people who use speech generating devices." International Journal of Speech-Language Pathology 13, no. 5 (October 25, 2010): 446–57. http://dx.doi.org/10.3109/17549507.2011.522254.

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6

Goldman, Amy. "Ten Funding Myths Demystified!" Perspectives of the ASHA Special Interest Groups 1, no. 12 (March 31, 2016): 6–9. http://dx.doi.org/10.1044/persp1.sig12.6.

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This article addresses 10 common misconceptions about the availability of funding and coverage for speech-generating devices (SGDs). Speech-language pathologists who will be evaluating individuals who can benefit from SGDs should become familiar with their responsibilities to ensure the process concludes with successful procurement of the recommended device. A reminder of ethical practices and additional resources are provided to the reader.

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Dukhovny, Elena, and Gloria Soto. "Speech Generating Devices and Modality of Short-term Word Storage." Augmentative and Alternative Communication 29, no. 3 (August 9, 2013): 246–58. http://dx.doi.org/10.3109/07434618.2013.815799.

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8

Kander, Mark. "Bottom Line: Medicare Requires Physician Visit for Speech-Generating Devices." ASHA Leader 18, no. 9 (September 2013): 28–29. http://dx.doi.org/10.1044/leader.bml.18092013.28.

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9

Yang, Hyun-Kieu. "A Meta-analysis of Communication Interventions Involving Speech-Generating Devices(SGD)." Journal of Special Children Education 18, no. 3 (September 2016): 143–68. http://dx.doi.org/10.21075/kacsn.2016.18.3.143.

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10

van der Meer, Larah, Debora Kagohara, Donna Achmadi, Mark F. O’Reilly, Giulio E. Lancioni, Dean Sutherland, and Jeff Sigafoos. "Speech-generating devices versus manual signing for children with developmental disabilities." Research in Developmental Disabilities 33, no. 5 (September 2012): 1658–69. http://dx.doi.org/10.1016/j.ridd.2012.04.004.

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11

Savaldi-Harussi, Gat, and Gloria Soto. "Early verbal categories and inflections in children who use speech-generating devices." Augmentative and Alternative Communication 34, no. 3 (July 3, 2018): 194–205. http://dx.doi.org/10.1080/07434618.2018.1490925.

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12

Savaldi-Harussi, Gat, Lyle Lustigman, and Gloria Soto. "The emergence of clause construction in children who use speech generating devices." Augmentative and Alternative Communication 35, no. 2 (April 3, 2019): 109–19. http://dx.doi.org/10.1080/07434618.2019.1584642.

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13

Thunberg, Gunilla, Annika Dahlgren Sandberg, and Elisabeth Ahlsén. "Speech-Generating Devices Used at Home by Children With Autism Spectrum Disorders." Focus on Autism and Other Developmental Disabilities 24, no. 2 (January 7, 2009): 104–14. http://dx.doi.org/10.1177/1088357608329228.

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14

Shillingsburg, M. Alice, Videsha Marya, Brittany L. Bartlett, and Taylor M. Thompson. "Teaching mands for information using speech generating devices: A replication and extension." Journal of Applied Behavior Analysis 52, no. 3 (June 3, 2019): 756–71. http://dx.doi.org/10.1002/jaba.579.

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15

Marya, Videsha, Sarah Frampton, and Alice Shillingsburg. "Matrix training to teach tacts using speech generating devices: Replication and extension." Journal of Applied Behavior Analysis 54, no. 3 (March 5, 2021): 1235–50. http://dx.doi.org/10.1002/jaba.819.

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16

Chung, Yun-Ching, and Erik W. Carter. "Promoting Peer Interactions in Inclusive Classrooms for Students Who Use Speech-Generating Devices." Research and Practice for Persons with Severe Disabilities 38, no. 2 (June 2013): 94–109. http://dx.doi.org/10.2511/027494813807714492.

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17

Ballin, Liora, Susan Balandin, Roger J. Stancliffe, and Leanne Togher. "The views of people who use speech generating devices on mentoring new learners." Disability and Rehabilitation: Assistive Technology 7, no. 1 (April 18, 2011): 63–74. http://dx.doi.org/10.3109/17483107.2011.573438.

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18

Dukhovny, Elena, and Jennifer J. Thistle. "An exploration of motor learning concepts relevant to use of speech-generating devices." Assistive Technology 31, no. 3 (November 29, 2017): 126–32. http://dx.doi.org/10.1080/10400435.2017.1393845.

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19

Brodhead, Matthew T., Lauren F. Brouwers, Emma S. Sipila-Thomas, and Mandy J. Rispoli. "A Comparison of Manual Sign and Speech Generating Devices in the Natural Environment." Journal of Developmental and Physical Disabilities 32, no. 5 (January 6, 2020): 785–800. http://dx.doi.org/10.1007/s10882-019-09720-1.

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20

Ball, Laura J., Gary L. Pattee, Lewis Golinker, and David R. Beukelman. "Speech-Generating Device Funding and Patterns of Acquisition for Persons With Amyotrophic Lateral Sclerosis." Perspectives on Augmentative and Alternative Communication 24, no. 4 (September 2015): 155–60. http://dx.doi.org/10.1044/aac24.4.155.

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People with such severe and complex communication needs that they require speech-generating devices (SGDs) to meet daily communication needs come from all age groups and socioeconomic backgrounds (Beukelman & Mirenda, 2013). Among this group are people eligible for Medicare, which will provide payment for a percentage (typically 80 percent) of covered health care costs, including SGDs. Medicare eligibility extends to people age 65 and older and younger people who became disabled from non-work related causes. In this article, the authors first review Medicare coverage for augmentative and alternative communication (AAC) devices beginning in the 1980s and subsequently document the SGD acquisition and access of 64 people with amyotrophic lateral sclerosis (ALS) whose speech became so severely limited that they required an SGD to support functional communication.

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21

Gracia, Narissa, Anna F. Rumbach, and Emma Finch. "A survey of speech-language pathology treatment for non-progressive dysarthria in Australia." Brain Impairment 21, no. 2 (March 17, 2020): 173–90. http://dx.doi.org/10.1017/brimp.2020.3.

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AbstractAim:To identify management practices of Australian speech-language pathologists (SLPs) in the treatment of non-progressive dysarthria using a subsystem approach, and to explore SLPs’ consideration and implementation of the theoretical underpinnings of non-progressive dysarthria management.Method:A 39-item online survey was distributed to Australian SLPs, with 80 responses suitable for data analysis.Results:Practices of SLPs were variable for the management of the speech subsystems. The Lee Silverman Voice Treatment (LSVT®) was the most commonly used manualised treatment program, and was employed by 63.77% of respondents. Almost all SLPs (>88%) provided strategies to improve functional communication. There was no clear preference for low tech alternative and augmentative communication (AAC) devices. Speech generating devices were the most commonly employed high tech device. Almost two-thirds of respondents used non-speech oral motor exercises (NSOMEs) in treatment. SLPs had varied frequencies and models of service delivery for intervention. SLPs valued interventions targeting the activity and participation domains of the ICF, however this was restricted by the treatment context and resources available. The majority of SLPs (92.06%) were aware of the principles of motor learning, however many were unsure regarding the specifics of implementation.Conclusion:There is a clear need for further research into the efficacy of treatment techniques to guide decision-making.

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22

Westley, Michelle, Dean Sutherland, and H. Timothy Bunnell. "Voice Banking to Support People Who Use Speech-Generating Devices: New Zealand Voice Donors' Perspectives." Perspectives of the ASHA Special Interest Groups 4, no. 4 (August 15, 2019): 593–600. http://dx.doi.org/10.1044/2019_pers-sig2-2018-0011.

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Purpose Voice banking is the process of recording an individual's speech to create a personalized synthetic voice to use on speech-generating augmentative and alternative communication devices. This study set out to examine the experience of healthy voice donors during the ModelTalker voice banking process and to identify specific issues related to voice banking in the New Zealand context. Method Eight healthy adults and 2 children completed the ModelTalker voice banking protocol. All participants completed a questionnaire about their voice banking experience, including the length of time required as well as the ease and the technical aspects of the process. Results The median time taken to complete the voice banking process was 5 hr 30 min (range: 3 hr 10 min to 11 hr). Questionnaire responses included themes related to the voice banking process, such as increased awareness of voice banking and its benefits, positive features and challenges of the ModelTalker process, and potential adaptations for the New Zealand context. Conclusions The findings support the use of ModelTalker with New Zealand speakers and inform development of voice banking protocols. The voices created as part of this study are available for people using speech-generating devices who want to use New Zealand–accented voices. Future research is needed to investigate the voice banking experiences of clinical populations, such as people with amyotrophic lateral sclerosis.

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23

Wilkins, Julia, and Amy Ratajczak. "Developing Students' Literacy Skills Using High-Tech Speech-Generating Augmentative and Alternative Communication Devices." Intervention in School and Clinic 44, no. 3 (January 2009): 167–72. http://dx.doi.org/10.1177/1053451208326050.

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24

Lüke, Carina. "Impact of speech-generating devices on the language development of a child with childhood apraxia of speech: a case study." Disability and Rehabilitation: Assistive Technology 11, no. 1 (April 29, 2014): 80–88. http://dx.doi.org/10.3109/17483107.2014.913715.

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25

Copple, Karen, Rajinder Koul, Devender Banda, and Ellen Frye. "Using Video Modeling Intervention and Speech Generating Devices To Teach Requesting Behaviors to Persons With Autism." Perspectives on Augmentative and Alternative Communication 20, no. 4 (December 2011): 109–13. http://dx.doi.org/10.1044/aac20.4.109.

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Abstract One of the instructional techniques reported in the literature to teach communication skills to persons with autism is video modeling (VM). VM is a form of observational learning that involves watching and imitating the desired target behavior(s) exhibited by the person on the videotape. VM has been used to teach a variety of social and communicative behaviors to persons with developmental disabilities such as autism. In this paper, we describe the VM technique and summarize the results of two single-subject experimental design studies that investigated the acquisition of spontaneous requesting skills using a speech generating device (SGD) by persons with autism following a VM intervention. The results of these two studies indicate that a VM treatment package that includes a SGD as one of its components can be effective in facilitating communication in individuals with autism who have little or no functional speech.

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26

Suk, Soo‐Young, and Hyun‐Yeol Chung. "A speech and character combined recognition engine for mobile devices." International Journal of Pervasive Computing and Communications 4, no. 2 (June 27, 2008): 232–49. http://dx.doi.org/10.1108/17427370810890409.

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PurposeThe purpose of this paper is to describe a speech and character combined recognition engine (SCCRE) developed for working on personal digital assistants (PDAs) or on mobile devices. Also, the architecture of a distributed recognition system for providing a more convenient user interface is discussed.Design/methodology/approachIn SCCRE, feature extraction for speech and for character is carried out separately, but the recognition is performed in an engine. The client recognition engine essentially employs a continuous hidden Markov model (CHMM) structure and this CHMM structure consists of variable parameter topology in order to minimize the number of model parameters and to reduce recognition time. This model also adopts the proposed successive state and mixture splitting (SSMS) method for generating context independent model. SSMS optimizes the number of mixtures through splitting in mixture domain and the number of states through splitting in time domain.FindingsThe recognition results show that the developed engine can reduce the total number of Gaussian up to 40 per cent compared with the fixed parameter models at the same recognition performance when applied to speech recognition for mobile devices. It shows that SSMS can reduce the size of memory for models to 65 per cent and that for processing to 82 per cent. Moreover, the recognition time decreases 17 per cent with the SMS model while maintaining the recognition rate.Originality/valueThe proposed system will be very useful for many on‐line multimodal interfaces such as PDAs and mobile applications.

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27

Alzrayer, Nouf M., and Devender R. Banda. "Implementing Tablet-Based Devices to Improve Communication Skills of Students With Autism." Intervention in School and Clinic 53, no. 1 (March 10, 2017): 50–57. http://dx.doi.org/10.1177/1053451217692569.

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Students with autism spectrum disorder (ASD) have difficulties in communication that limit their opportunities to participate in daily living and educational activities. Augmentative alternative communication is one of the strategies used to strengthen the communication skills of students with limited communication skills. Students with ASD commonly use handheld devices for communication. This article provides guidelines for special education teachers to support the implementation of tablet-based speech-generating devices in their classrooms. These guidelines may help special education teachers through the process of creating a student’s profile, conducting preference assessments, programming devices, and providing instruction in natural contexts.

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28

van der Meer, Larah A. J., and Mandy Rispoli. "Communication interventions involving speech-generating devices for children with autism: A review of the literature." Developmental Neurorehabilitation 13, no. 4 (July 14, 2010): 294–306. http://dx.doi.org/10.3109/17518421003671494.

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29

Thunberg, Gunilla, Elisabeth Ahlsén, and Annika Dahlgren Sandberg. "Children with autistic spectrum disorders and speech‐generating devices: Communication in different activities at home." Clinical Linguistics & Phonetics 21, no. 6 (January 2007): 457–79. http://dx.doi.org/10.1080/02699200701314963.

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30

Oxley, Judith, and Yan Ma. "Considerations for Chinese text input methods in the design of speech generating devices: a tutorial." Clinical Linguistics & Phonetics 34, no. 4 (September 3, 2019): 366–87. http://dx.doi.org/10.1080/02699206.2019.1652934.

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31

Bryen, Diane Nelson. "Communication in times of natural or man-made emergencies: the potential of speech-generating devices." International Journal of Emergency Management 7, no. 1 (2010): 17. http://dx.doi.org/10.1504/ijem.2010.032041.

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32

Frampton, Sarah E., M. Alice Shillingsburg, and Paul J. Simeone. "Feasibility and Preliminary Efficacy of Direct Instruction for Individuals With Autism Utilizing Speech-Generating Devices." Behavior Analysis in Practice 13, no. 3 (February 11, 2020): 648–58. http://dx.doi.org/10.1007/s40617-020-00412-3.

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33

Chung, Yun-Ching, and Karen H. Douglas. "A Peer Interaction Package for Students with Autism Spectrum Disorders who Use Speech-Generating Devices." Journal of Developmental and Physical Disabilities 27, no. 6 (November 18, 2015): 831–49. http://dx.doi.org/10.1007/s10882-015-9461-1.

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34

Młynarczyk-Karabin, Ewelina. "Nowe technologie a funkcjonowanie osób z niepełnosprawnością w społeczeństwie." Kwartalnik Pedagogiczny 64, no. 3 (253) (October 25, 2019): 239–51. http://dx.doi.org/10.5604/01.3001.0013.5546.

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Dexterity and abilities are a sign of human growth. Lack or deficiency thereof, which is typical for people with disabilities – may lead to significant deficiency of activities of such persons in society. In the article the author describes the new technologies which are useful for people with disabilities. Attention is drawn to the availability of various technologies and devices on the market and for people with disabilities. The article describes, among others, speech generating devices or eye-trackers, which can help persons with various types of disability e.g. deaf or immobile persons.

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35

Lorah, Elizabeth R. "Evaluating the iPad Mini® as a Speech-Generating Device in the Acquisition of a Discriminative Mand Repertoire for Young Children With Autism." Focus on Autism and Other Developmental Disabilities 33, no. 1 (October 13, 2016): 47–54. http://dx.doi.org/10.1177/1088357616673624.

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There has been an increased interest in research evaluating the use of handheld computing technology as speech-generating devices (SGD) for children with autism. However, given the reliance on single-subject research methodology, replications of these investigations are necessary. This study presents a replication with variation, of a method for the acquisition of picture-symbol discrimination during mand training, while using the iPad® and application Proloquo2Go™ as an SGD in young children with autism. In a four-phased training procedure, three children with a diagnosis of autism acquired the ability to mand and discriminate between four picture-symbols on the screen of the device, while using the iPad Mini® as an SGD. In addition, for all three participants, the acquired repertoires maintained following the discontinuation of training. These results provide continued support for the use of handheld computing devices as SGD for children with autism.

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36

Tegler, Helena, Mia Pless, Monica Blom Johansson, and Karin Sonnander. "Speech and language pathologists’ perceptions and practises of communication partner training to support children’s communication with high-tech speech generating devices." Disability and Rehabilitation: Assistive Technology 14, no. 6 (May 23, 2018): 581–89. http://dx.doi.org/10.1080/17483107.2018.1475515.

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37

Gillette, Yvonne. "Word-Finding Support From Mobile Technology Benefits a Woman With Aphasia." Perspectives on Augmentative and Alternative Communication 24, no. 1 (January 2015): 26–39. http://dx.doi.org/10.1044/aac24.1.26.

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Mobile technology provides a solution for individuals who require augmentative and alternative intervention. Principles of augmentative and alternative communication assessment and intervention, such as feature matching and the participation model, developed with dedicated speech-generating devices can be applied to these generic mobile technologies with success. This article presents a clinical review of an adult with aphasia who reached her goals for greater communicative participation through mobile technology. Details presented include device selection, sequence of intervention, and funding issues related to device purchase and intervention costs. Issues related to graduate student clinical education are addressed. The purpose of the article is to encourage clinicians to consider mobile technology when intervening with an individual diagnosed with mild receptive and moderate expressive aphasia featuring word-finding difficulties.

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38

Sigafoos, Jeff, Vanessa A. Green, Donna Payne, Seung-Hyun Son, Mark O'Reilly, and Giulio E. Lancioni. "A Comparison of Picture Exchange and Speech-Generating Devices: Acquisition, Preference, and Effects on Social Interaction." Augmentative and Alternative Communication 25, no. 2 (January 2009): 99–109. http://dx.doi.org/10.1080/07434610902739959.

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39

Ripat, Jacquie, Michèle Verdonck, Carly Gacek, and Shelly McNicol. "A qualitative metasynthesis of the meaning of speech-generating devices for people with complex communication needs." Augmentative and Alternative Communication 35, no. 2 (September 19, 2018): 69–79. http://dx.doi.org/10.1080/07434618.2018.1513071.

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40

Stern, Steven E., Chelsea M. Chobany, Alexander A. Beam, Brittany N. Hoover, Thomas T. Hull, Melissa Linsenbigler, Courtney Makdad-Light, and Courtney N. Rubright. "Use of speech generating devices can improve perception of qualifications for skilled, verbal, and interactive jobs." Work 56, no. 2 (March 14, 2017): 199–211. http://dx.doi.org/10.3233/wor-172489.

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41

Happ, Mary Beth, Tricia K. Roesch, and Sarah H. Kagan. "Patient Communication Following Head and Neck Cancer Surgery: A Pilot Study Using Electronic Speech-Generating Devices." Oncology Nursing Forum 32, no. 6 (November 1, 2005): 1179–87. http://dx.doi.org/10.1188/05.onf.1179-1187.

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42

Brock, Kris L., Rajinder Koul, Melinda Corwin, and Ralf W. Schlosser. "The psychometric properties of the communicative competence scale for individuals with Aphasia using speech-generating devices." Aphasiology 33, no. 5 (January 8, 2019): 520–43. http://dx.doi.org/10.1080/02687038.2018.1561639.

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43

George, Catherine, and Faye Warren. "Mentoring as a Communication Coach in a Public School Setting." Perspectives on Augmentative and Alternative Communication 21, no. 3 (September 2012): 115–21. http://dx.doi.org/10.1044/aac21.3.115.

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Abstract People using speech generating devices face many challenges, one of which is the lack of role models. They seldom have the opportunity to meet and interact with other proficient SGD users. This article addresses key considerations for initiating an AAC mentoring position as a Communication Coach in a public school setting. Outcomes and considerations to facilitate the likelihood of success are discussed from both administrative and mentor perspectives.

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44

Sennott, Samuel C., Reny Ferrari, Gayle McLernon, and Deborah Lesher. "The Three Definitions of Application for AAC Intervention." Perspectives of the ASHA Special Interest Groups 1, no. 12 (March 31, 2016): 99–107. http://dx.doi.org/10.1044/persp1.sig12.99.

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This article will address an important issue related to speech-generating devices (SGDs) and augmentative and alternative communication (AAC) software applications (apps) in relation to the widespread adoption of mobile devices. This article will explore each of the three definitions of application including (a) a software application, (b) putting something into practice or operation, and (c) sustained effort. We hope that these three “app” definitions and discussion of our use of the Student, Environments, Tasks, and Tools (SETT) Framework can be a quick, easy, and memorable way to convey to families, therapists, and teachers that tools are made powerful when they are combined with good teaching and processes that sustain the use of AAC. A review of The Centers for Medicare and Medicaid Services updated 2015 National Coverage Decision for SGDs will be included, highlighting the major change to this updated coverage which includes the expansion of the definition of speech.

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45

Kamruzzaman, M. M. "Arabic Sign Language Recognition and Generating Arabic Speech Using Convolutional Neural Network." Wireless Communications and Mobile Computing 2020 (May 23, 2020): 1–9. http://dx.doi.org/10.1155/2020/3685614.

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Sign language encompasses the movement of the arms and hands as a means of communication for people with hearing disabilities. An automated sign recognition system requires two main courses of action: the detection of particular features and the categorization of particular input data. In the past, many approaches for classifying and detecting sign languages have been put forward for improving system performance. However, the recent progress in the computer vision field has geared us towards the further exploration of hand signs/gestures’ recognition with the aid of deep neural networks. The Arabic sign language has witnessed unprecedented research activities to recognize hand signs and gestures using the deep learning model. A vision-based system by applying CNN for the recognition of Arabic hand sign-based letters and translating them into Arabic speech is proposed in this paper. The proposed system will automatically detect hand sign letters and speaks out the result with the Arabic language with a deep learning model. This system gives 90% accuracy to recognize the Arabic hand sign-based letters which assures it as a highly dependable system. The accuracy can be further improved by using more advanced hand gestures recognizing devices such as Leap Motion or Xbox Kinect. After recognizing the Arabic hand sign-based letters, the outcome will be fed to the text into the speech engine which produces the audio of the Arabic language as an output.

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46

Robinson, Nancy B. "Internet Resources for Augmentative and Alternative Communication in Early Intervention and Early Childhood Services." Perspectives on Augmentative and Alternative Communication 18, no. 1 (April 2009): 28–32. http://dx.doi.org/10.1044/aac18.1.28.

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Abstract The development and implementation of augmentative and alternative communication (AAC) tools for children in the earliest stages of communication and language development may include a range of communication modalities that include gesture, vocalization, actual objects, photos, picture icons, Speech Generating Devices (SGDs), and higher-tech AAC devices. Internet resources for AAC in early intervention settings include assessment tools, communication aids, early literacy supports, educational tools for professionals and families and much more. Some of the key resources that have been reviewed by a team of professionals through the Supporting Early Education Development Systems (SEEDS) Project in the Sacramento County Office of Education, Sacramento, California, are described.

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47

Kagohara, Debora M., Larah van der Meer, Donna Achmadi, Vanessa A. Green, Mark F. O’Reilly, Giulio E. Lancioni, Dean Sutherland, Russell Lang, Peter B. Marschik, and Jeff Sigafoos. "Teaching picture naming to two adolescents with autism spectrum disorders using systematic instruction and speech-generating devices." Research in Autism Spectrum Disorders 6, no. 3 (July 2012): 1224–33. http://dx.doi.org/10.1016/j.rasd.2012.04.001.

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48

Adams, Kim, and Al Cook. "Access to hands-on mathematics measurement activities using robots controlled via speech generating devices: three case studies." Disability and Rehabilitation: Assistive Technology 9, no. 4 (August 19, 2013): 286–98. http://dx.doi.org/10.3109/17483107.2013.825928.

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49

Crisp, Cheryl, Claire Burke Draucker, and Marsha L. Cirgin Ellett. "Barriers and facilitators to children's use of speech-generating devices: a descriptive qualitative study of mothers' perspectives." Journal for Specialists in Pediatric Nursing 19, no. 3 (March 18, 2014): 229–37. http://dx.doi.org/10.1111/jspn.12074.

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50

Kovacs, Thomas, and Katya Hill. "Language Samples From Children Who Use Speech-Generating Devices: Making Sense of Small Samples and Utterance Length." American Journal of Speech-Language Pathology 26, no. 3 (August 15, 2017): 939–50. http://dx.doi.org/10.1044/2017_ajslp-16-0114.

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Purpose Mean length of utterance in morphemes (MLUm) is underreported in people who use augmentative and alternative communication (AAC). MLUm is difficult to measure in people who use AAC because of 2 challenges described in literature: the challenge of small language samples (difficulty collecting representative samples) and the challenge of transcribing short utterances (difficulty transcribing 1-morpheme utterances). We tested solutions to both challenges in a corpus of language samples from children who use speech-generating devices. Method The first challenge was addressed by adjusting the length of the sampling window to obtain representative language samples. The second challenge was addressed by using mean syntactic length (MSL) as an alternative to MLUm. Results A 24-hour sample window consistently failed to yield representative samples. An extended 1-month sample window consistently yielded representative samples. A significant positive prediction of MLUm by MSL was found in a normative sample. Observed measures of MSL were used to predict MLUm in representative language samples from children who use AAC. Conclusions Valid measures of utterance length in people who use AAC can be obtained using extended sampling windows and MSL. Research is needed to characterize the strengths and limitations of both solutions.

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