Impact of the<i><b>LSVT</b></i>on vowel articulation and coarticulation in Parkinson’s disease
Bibliographic record
Abstract
The purpose of this study was to investigate the impact of the Lee Silverman Voice Treatment (LSVT®) on vowel articulation and consonant-vowel (C-V) coarticulation in dysarthric speakers with Parkinson's disease (PD). Nine Quebec French speakers diagnosed with idiopathic PD underwent the LSVT®. Speech characteristics were compared before and after treatment. Vowel articulation was measured using acoustic vowel space and calculated with the first (F1) and second formant (F2) of the vowels /i/, /u/ and /a/. C-V coarticulation was measured using locus equations, an acoustic metric based on the F2 transitions within vowels in relation to the preceding consonant. The relationship between these variables, speech loudness and vowel duration was also analysed. Results showed that vowel contrast increased in F1/F2 acoustic space after administration of the LSVT®. This improvement was associated with the gain in speech loudness and longer vowel duration. C-V coarticulation patterns between consonant contexts showed greater distinctiveness after the treatment. This improvement was associated with the gain in speech loudness only. These results support the conclusions of previous studies investigating the relationship between the LSVT®, speech loudness and articulation in PD. These results expand clinical understanding of the treatment and indicate that loud speech changes C-V coarticulation patterns. Clinical applications and theoretical considerations 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".