Cross-lingual transferability of voice analysis models: a Parkinson’s Disease case study
Bibliographic record
Abstract
Language and speech are one of the most complex human activities, involving cognitive-linguistic processes, motor speech planning, programming, control, phonetic integrit, and finally neuromuscular execution."Language" refers to a core system that enables the user to assign or decode a symbol to an object or concept that one wishes to convey or comprehend, and also to apply appropriate grammatical rules to arrange or decipher phrases and sentences.In this definition, language is the "engine" of communication.When language is impaired as a result of injury to the brain, the disorder is termed "aphasia.On the other hand, "speech" is the physical process of speaking involving lungs, trachea, breathing muscles, vocal cords, mouth, tongue, facial muscles, and it is defined as the mechanism by which language is orally expressed and articulated.Some individuals may have a motor speech deficit, making difficult to produce words, e.g., "dysarthria" or to coordinate complex movements for articulation e.g., "apraxia of speech".Two specific brain areas are related to understand and generate speech: Broca's area related to speech production and articulation, and Wernicke's area responsible for speech comprehension.However, the organization of language and the comprehension and production of speech are mediated by a much broader expanse of neural networks that covers a large number of cortical and subcortical regions and their interconnecting fiber pathway, e.g., inferior and middle frontal gyri, superior precentral gyrus of the insula, basal ganglia, posterior middle temporal gyrus, cerebellum, as demonstrated by new neuroimaging techniques.Some disorders of nervous system may manifest with language/ speech changes.In the last decade, language in neurodegenerative diseases has been recognized as a specific marker not only for distinguishing different language isolated syndromes, or primary progressive aphasia (PPA) and its variants, but also for diagnosing various neurodegenerative disorders characterized by language impairment amongst other cognitive deficit, and neurological signs and symptoms, as Parkinson, Alzheimer, Amyotrophic Lateral Sclerosis, Multiple Sclerosis.The study of language processing with a better knowledge of the language network, due to imaging techniques, may contribute to a greater understanding of the extension of neurodegenerative process that characterize such diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".