The study examines how speech preprocessing—such as enhancement, sample selection, and demographic balancing—affects Alzheimer’s disease detection models that use the Pitt Corpus. Experiments reveal that while speech‑enhanced datasets boost in‑domain accuracy, they diminish cross‑dataset robustness and introduce class imbalance and prediction shifts, even when training and testing enhancements are matched. Large audio‑language models show similar sensitivity, indicating that cleaner speech does not guarantee better real‑world performance.
Speech-based Alzheimer's disease (AD) detection increasingly relies on speech-enhanced and curated versions of the Pitt Corpus, where speech enhancement, sample selection, and demographic balancing ar...
arXiv:2602. 11177v2 Announce Type: replace-cross Abstract: Reliable early detection of Alzheimer's disease (AD) is challenging, particularly due to the limited availability of labeled data.
arXiv:2607. 10168v1 Announce Type: cross Abstract: It is still hard to find Alzheimer's disease (AD) early, especially when neuroimaging is expensive or tools that depend on language are not available.
arXiv:2608. 07378v1 Announce Type: cross Abstract: Early diagnosis of Alzheimer's disease (AD) is critical for enabling timely interventions that may slow disease progression and improve patient outcomes.
The development of multilingual Alzheimer's Disease Dementia (AD) detection models presents significant challenges due to the resource-intensive and time-consuming nature of language-specific model training. We propose a novel solution using cross-language training to detect AD in languages beyond those used for model training.
arXiv:2603. 15988v3 Announce Type: replace-cross Abstract: Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies.
arXiv:2606. 17339v1 Announce Type: new Abstract: Speech offers a uniquely informative window into health by simultaneously engaging neurological, motor, respiratory, and vocal systems.
The paper addresses the challenge of within-class variation in Alzheimer’s Disease (AD) detection, where individuals with the same diagnosis can show differing levels of cognitive impairment. It introduces two methods—Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe)—that estimate sample-specific AD probabilities to better capture this heterogeneity. Experiments on the ADReSS and CU-MARVEL datasets demonstrate that these scores correlate with independent cognitive assessments and enhance AD detection performance.
arXiv:2502. 03484v3 Announce Type: replace-cross Abstract: Dementia encompasses a group of syndromes that impair cognitive functions such as memory, reasoning, and the ability to perform daily activities.
arXiv:2607. 21496v1 Announce Type: cross Abstract: Cognitive impairment (CI) is a growing public health concern.
arXiv:2510. 20441v2 Announce Type: replace-cross Abstract: Neural audio codecs have largely promoted the application of language models (LMs) for speech applications.
arXiv:2607. 23977v1 Announce Type: cross Abstract: Acoustic biomarkers show promise for detecting Alzheimer's Disease (AD), yet whether the cues driving diagnostic AI align with those salient to human listeners is underexplored across languages and genders, where pathological markers and perceptual strategies differ.