Listener deficits in hypokinetic dysarthria: Which cues are most important in speech segmentation?
Bibliographic record
Abstract
Listeners use prosodic cues to help them quickly process running speech. In English, listeners effortlessly use strong syllables to help them to find words in the continuous stream of speech produced by neurologically-intact individuals. However, listeners are not always presented with speech under such ideal circumstances. This thesis explores the question of word segmentation of English speech under one of these less ideal conditions; specifically, when the speaker may be impaired in his/her production of strong syllables, as in the case of hypokinetic dysarthria. Further, we attempt to discern which acoustic cue(s) are most degraded in hypokinetic dysarthria and the effect that this degradation has on listeners' segmentation when no additional semantic or pragmatic cues are present. Two individuals with Parkinson's disease, one with a rate disturbance and one with articulatory disruption, along with a typically aging control, were recorded repeating a series of nonsense syllables. Young adult listeners were then presented with recordings from one of these three speakers producing non-words (imprecise consonant articulation, rate disturbance, and control). After familiarization, the listeners were asked to rate the familiarity of the non-words produced by a second typically aging speaker. Results indicated speakers with hypokinetic dysarthria were able to modulate their intensity and duration for stressed and unstressed syllables in a way similar to that of control speakers. In addition, their mean and peak fundamental frequency for both stressed and unstressed syllables were significantly higher than that of the normally aging controls. ANOVA results revealed a marginal main effect of frequency in normal and consonant conditions for word versus nonwords listener ratings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".