Psycholinguistic speech processing assessment for adults: \nDevelopment and case series \n
Bibliographic record
Abstract
In educational institutions there are a significant number of young adults with speech, language and literacy problems. Nevertheless, due to a lack of assessment tools, difficulties are often not recognised which in turn limits access to possible supports. The specific objective of this study was to develop a comprehensive speech processing skills assessment battery for native English-speaking adults, taking psycholinguistics into account. The assessment tool consists of subtests that assess auditory discrimination of non-words and non-word repetition, reading and spelling of non-words, and spoonerisms with non- and real words. \nNormative data from 101 English-speaking adults (age 18-35 years) were collected and analysed in terms of general psychometric properties. Further in depth analyses look at the nature of mistakes and reaction time of participants. Moreover, a case series of participants who stammer (N=6) was conducted to test the speech processing assessment in regards to profiling existing speech difficulties and comparing these profiles to norm data. \nResults support the establishment of objectivity, validity and reliability of the assessment tool, but also highlight important factors which need to be investigated in more detail. Results concerning the case studies showed individual differences of performances compared to the norm data which can be explained by theoretical knowledge about stammering. \nOutcomes encourage the usage of the assessment tool for research (e.g. comparison of speech processing profiles in adults with speech disorders) as well as the possibility of further development for clinical and educational settings (e.g. the development of specific disability support). A next step of this programme of work could be to modify the assessment tool based on analysed outcomes. Moreover, deeper investigation of people experiencing speech difficulties could follow to support the profiling of adults with persistent developmental speech difficulties in, for example, higher education. \n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".