1 / 30

Workshop: FluencyBank , Child Language Assessment Project (CLASP), and Dialect Variation

Workshop: FluencyBank , Child Language Assessment Project (CLASP), and Dialect Variation. Nan Bernstein Ratner Julianne Garbarino Veronica Builes Courtney Overton Pamela Dominguez The University of Maryland, College Park. Acknowledgements.

weiner
Download Presentation

Workshop: FluencyBank , Child Language Assessment Project (CLASP), and Dialect Variation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Workshop: FluencyBank, Child Language Assessment Project (CLASP), and Dialect Variation Nan Bernstein Ratner Julianne Garbarino Veronica Builes Courtney Overton Pamela Dominguez The University of Maryland, College Park

  2. Acknowledgements Funding: NIHHD 9R01HD082736-11 (Brian MacWhinney, PI). Corpus-based assessment of child language; Consultant. 2014-2019, NIDCD: 1 R01 DC015494-01 (Brian MacWhinney, co-PI). A shared database for the study of the development of language fluency. , NSF BCS-1626300/1626294: The development of language fluency across childhood. N. Bernstein Ratner (PI) & B. MacWhinney, Co-I (Collaborative Research), 2016-2019. NIDCD: 1R01DC016076-01(Nan Bernstein Ratner, PI): 2018-2023) Validation and norming of children's expressive language sample analysis measures. $1,722,800

  3. My connection with Brian • Goes back to when he and Catherine Snow were scarfing up data for CHILDES right after its inception, and I had helpfully typed my transcripts • So my work goes back to the beginning… • Sharing data with TalkBank is like the expression “Love isn’t love until you give it away” – my original data has now been used in over 100 studies and publications • It’s one of my most cited “contributions to science” • So, junior [and senior] researchers, share your data  • More on “kumbaya science” tomorrow…

  4. Overview • I will introduce the FluencyBank project and its resources: • Database • Fluency codes • Teaching resources • FluCalc utility • I will introduce CLASP (the Child Language Assessment Project) • Uses CHILDES NA database • Kideval utility to derive 40+ language sample analysis (LSA) measures • Real-time reference norming for children 2-6 years of age • I will discuss the challenges of LSA transcription and measures when working with child (or adult) speakers of African-American English

  5. FluencyBank Resources and Utilities • FluencyBank was funded in 2016 by joint funding from the National Science Foundation and the NIDCD to Brian MacWhinney and Nan Bernstein Ratner. • Its goals are to study development of typical and atypical fluency profiles across childhood. • Four current thrusts: • Database for research cooperation • Coding conventions for disfluency • Software programs to analyze fluency profiles for both clinical and research • Teaching/instructional resources • Emphasis on the ABC’s of stuttering: behavior, affect and cognition

  6. A sample of research hypotheses funded by the NSF/NIDCD grants: • How are fluency patterns seen in typical, language-delayed, stuttering and Spanish-English bilingual preschoolers similar or different? • What fluency profiles distinguish likelihood of recovery from childhood onset fluency disorder (stuttering) from persistence? • Do different fluency types (e.g., repetitions, “stalls”) seem to serve different functions? • Are they seen on different types of words? Or in different locations?

  7. Status of the database: a fast tour • Fluency research is carried out by a minority of research settings, even in CSD • A huge obstacle has been failure to adopt either a standard transcription platform (although use of SALT is common), or computable, transparent codes • Reliability of fluency coding notoriously poor – need for sonic linkage to improve and verify • Some major, groundbreaking longitudinal datasets of stuttering/typical children: • Illinois Stuttering Research Project (Ehud Yairi, Nicoline Ambrose and colleagues) – already posted, but in need of organization, linkage and fluency recoding • Purdue Stuttering Project (Anne Smith, Christine Weber and colleagues): currently in fluency recoding • Iowa Stuttering Project (pending) • HARD WORK TO TRANSFORM SALT CODES (unsystematic, imprecise) to CHAT, lots of lab hours • Other major data sets, including Weismer (for fluency in LI), Hoff (fluency in bilingualism)

  8. Survey of FluencyBank site

  9. How coding for fluency works: • Needed to develop a system that • would work in any orthography, • CLAN’s mor parser can “see through” • includes severity information (e.g., numbers of iterated segments) • Resulting codes (live since late 2016); see SLP Guide and CHAT manuals:

  10. Software to create fluency profiles: FluCalc • FluCalc relies on the new CHAT codes • If transcript is video/audio linked, can derive speech rate • Computes raw and proportioned values (over words or syllables) for: • Typical disfluencies (e.g., unfilled and filled pauses, phrase repetitions and revisions) • Atypical “stutterlike” disfluencies, SLDs: (blocks, consonant prolongations, sound/syllable/monosyllabic whole word repetitions, vocal breaks) • Differential diagnosis of “developmental disfluency”/stuttering (now called childhood onset fluency disorder in DSM-V, ICD-11), using weighted disfluency score (Ambrose & Yairi, 2005). • “Cross-tier” analysis of grammatical class and fluency (content/function word ratio of disfluent items) • Already referenced in major grad texts in stuttering! 

  11. FluCalc Demonstration: • I will run one of my original children who stutter on FluCalc

  12. Sample FluCalc output

  13. List of measures in FluCalc:Each value is reported in raw counts and proportions (over words OR syllables) • Total utterances in the sample • Total intended words, as identified by MOR • # Prolongation: raw count of sound prolongations • # Broken word • # Block • # PWR (Part-word repetition) • # PWR RU (Repetition units): iterations, or number of excess repetitions in a part-word repetition. • # Phonological fragment –abandoned word attempts, e.g. &+fr- tadpole, where the speaker appears to change word choices • # WWR (whole word repetition) • # WWR RU (repetition units; please see PWR above)

  14. FluCalc output, continued • # Phrase repetitions •# Word revisions •# Phrase revisions •# Pauses (hesitations) • # Pause duration (if specified by coder) •# Filled pauses •# SLD (stutter-like disfluency); summing over categories in columns Prolongations through whole-word repetitions (WWR), with the exception of columns reporting repetition units (RUs) •# TD (typical disfluencies): summing categories in columns labeled Phrase Repetitions through Filled Pauses) • # Total (SLD+TD): this sums all forms of disfluency, both stutter-like and typical, seen in the sample

  15. Most critically, a reference value to distinguish normal disfluency from stuttering • Weighted SLD. This is an adapted version of the SLD formula for distinguishing between typical disfluency and stuttering profiles in young children. It was originated by Yairi & Ambrose (1999, 2005). • This formula multiplies the SUM of part-word and whole-word repetitions by the MEAN of the observed repetition units in the sample; it then adds this value to TWICE the sum of prolongations and blocks. • A weighted score greater than 4.0 is considered greater than values obtained from typically fluent children and merits concern.

  16. Options to analyze disfluency loci linguistically • Switch to identify part-of-speech for stuttered words • Current project: the supposed content-function “switch” between childhood and adulthood in features of stuttered words.

  17. Switching gears: CLASP • AphasiaBank laid the groundwork for a bundled utility to profile speakers with aphasia (EVAL). • KidEval was developed shortly thereafter. • What KidEval tracks: DEMO

  18. A longstanding set of problems in child language sample analysis (LSA) • Lots of proposed measures, but: • Few get used in clinical practice --- too time-consuming • Normative values are VERY poor --- small populations, little diversity • SALT has only 87 children under age 6!!!! • Almost all LSA measures are biased against speakers of non-mainstream American English (MAE). • A set of potential solutions: • Automate the analyses (many were pre-existing, but some are novel, such as SUGAR [Pavelko & Owens, 2018). • Use CHILDES archive to re”norm” the measures

  19. Comparing options:

  20. Comparing options (continued)

  21. Running KidEval and looking at output

  22. CLASP challenges: Variable success with conventional LSA measures • From Overton, Perry & Builes (this meeting): ability to distinguish Ellis Weismer children with LD from TD children

  23. Your KidEval printout (Excel):We’re sorry it’s so small … it provides a LOT of data

  24. Like Eval, KidEval now also produces dynamic reference scores (~1000 children and growing) • Need to curate and annotate individual files for grammaticality • Sample report:

  25. A sticking point for all LSA proposals: dialect • Most LSA measures will “penalize” non-MAE speakers who do not realize final inflections, auxiliary and copular forms, etc. in speaking • Transcription challenges: • Pronunciation: non-MAE is signaled by grammar, lexical forms and phonology • Yet, CLAN is meant to ignore phonology on the main tier • “Deleted” forms – presumes standard MAE perspective • Norm challenges: covariance of non-MAE and lower SES – when does “deficiency” overlap with “disorder”?

  26. CLASP work with AAVE is just starting • How to balance pronunciation on main tier with dialect variation? • We already encode some pronunciation variants (e.g., catenatives) • Possibility for automatic “dialect detector” for lexicalized dialect items in mor • “Omission” vs. “optional realization” of phonological and grammatical elements • Discussion of terminology – difference vs. disorder • At the same time, want to reduce clinical load in transcription – otherwise, system won’t be used.

  27. Our clinical goal: • A free, open-access, easily used, quick, and easily interpreted set of clinical tools • One step, no complicated command lines! • That has sufficient diversity to create robust norms that do not over- or under-identify children from 2-6 years, when standardized tests perform least well. • That take clinicians away from tedious assessment and back into the work they should be doing – intervention!!

  28. Not to scare Brian, but… • In the process of developing the CLASP proposal, we found a large population of people not well represented by any language sample analysis utilities:

  29. TEENBank!School-aged children from 6-18 have virtually no LSA data in TalkBank or elsewhere  And the work just keeps on rolling! Stay tuned…

  30. Thanks to: • Brian, of course, and Catherine Snow, for roping me in • Mary, for putting up with me and putting me up • LEONID! • Julianne Garbarino • Past and present project managers: Mark Baer, Veronica Builes, Courtney Overton • All of my students and past-students, who are really my colleagues in this effort • Our CLASP consultants: Jan Edwards, Barbara Pearson, Monique Mills

More Related