Listeners detect automatic anonymization in pathological speech at 91-93% accuracy with a 30-point perceived quality drop, yet clinical severity ratings stay nearly unchanged for dysarthria, dysglossia, and dysphonia.
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech,
3 Pith papers cite this work. Polarity classification is still indexing.
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The study shows clinical AI accuracy collapsing from 89% to 62% on X-rays under imperceptible adversarial perturbations and from 85% to 55% on clinical cases in Nigerian Pidgin and Yoruba-inflected English.
A survey catalogs text and speech resources for Hausa and Fongbe, documenting sizes, domains, licensing, and gaps including limited Fongbe text diversity and missing Hausa speech corpora.
citing papers explorer
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Perceptual implications of automatic anonymization in pathological speech
Listeners detect automatic anonymization in pathological speech at 91-93% accuracy with a 30-point perceived quality drop, yet clinical severity ratings stay nearly unchanged for dysarthria, dysglossia, and dysphonia.
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Adversarial Fragility and Language Vulnerability in Clinical AI: A Systematic Audit of Diagnostic Collapse Under Imperceptible Perturbations and Cross-Lingual Drift in Low-Resource Healthcare Settings
The study shows clinical AI accuracy collapsing from 89% to 62% on X-rays under imperceptible adversarial perturbations and from 85% to 55% on clinical cases in Nigerian Pidgin and Yoruba-inflected English.
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A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development
A survey catalogs text and speech resources for Hausa and Fongbe, documenting sizes, domains, licensing, and gaps including limited Fongbe text diversity and missing Hausa speech corpora.