A Case of Foreign Accent Syndrome Resulting in Regional Dialect
Bibliographic record
Abstract
BACKGROUND: Foreign Accent Syndrome (FAS) is a rare acquired syndrome following neurological damage that results in articulatory distortions that are commonly perceived as a "foreign" accent. The nature of the underlying deficit of FAS remains controversial. We present the first reported Canadian case study of FAS following a stroke. We describe a stroke patient, RD, who suffered an acute infarction to the left internal capsule, basal ganglia and frontal corona radiata. She was diagnosed as having FAS without any persistent aphasic symptoms. Family, friends, and health care professionals similarly described her speech as sounding like she had a Canadian East Coast accent, a reported change from her native Southern Ontario accent. METHOD: An investigation of this case was pursued, incorporating neuroimaging, neuropsychological and speech pathology assessments, and formalized linguistic analyses. RESULTS: Linguistic analyses confirmed that RD's speech does in fact have salient aspects of Atlantic Canadian English in terms of both prosodic and segmental characteristics. However, her speech is not entirely consistent with an Atlantic Canadian English accent. INTERPRETATION: The fact that RD's speech is perceived as a regional variant of her native language, rather than the "generic foreign accent" of FAS described elsewhere, suggests that the perceived "foreignness" in FAS is not primarily due to dysfluencies which indicate a non-native speaker, but rather due to very subtle motor-planning deficits which give rise to systemic changes in specific phonological segments. This has implications for the role of the basal ganglia in speech production.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".