Speech rate increase in primary progressive apraxia of speech and its cost on articulatory accuracy
Bibliographic record
Abstract
Impaired articulation (e.g., articulatory accuracy) and prosody (e.g., slow speech rate) are considered primary diagnostic criterions for apraxia of speech both in neurodegenerative and post-stroke contexts. The primary aim of this study was to investigate the ability of participants with primary progressive apraxia of speech (PPAOS), a neurodegenerative disease characterised by initially isolated progressive apraxia of speech, to increase speech rate and the interaction between articulatory accuracy and speech rate. The secondary aim was to investigate the effect of syllable frequency and structure on this interaction. Four speakers with PPAOS, and four sex- and age-matched healthy speakers (HS) read eight two-syllable words embedded two times in a ten-syllable carrier phrase. Syllable frequency and structure were manipulated for the first syllable of the target words and controlled for the second syllable. All sentences were produced at three different target speech rates (conditions): habitual, regular (five syllables/second), and fast (seven syllables/second). Prosodic measures for target words and sentences were computed based on acoustic analysis of speech rate. Articulatory measures for words and sentences were rated based on a perceptual assessment of articulatory accuracy. Results show slower speech rate and reduced articulatory accuracy in speakers with PPAOS compared to HS. Results suggest that speakers with PPAOS also have limited ability to increase their speech rate. Finally, results suggest that articulatory complexity influences speech rate but that the cost of speech rate increase on articulatory accuracy varies greatly across speakers with PPAOS and is not necessarily related to the extent of the increase when measured in a highly structured sentence production task. Theoretical and clinical implications of these findings are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".