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    • Novikova, Jekaterina (6)
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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Comparing Pre-trained and Feature-Based Models for Prediction of Alzheimer's Disease Based on Speech Balagopalan, Aparna; Eyre, Benjamin; Robin, Jessica... In: Front Aging Neurosci (2021) BASE Show details 2 To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection ... Balagopalan, Aparna; Eyre, Benjamin; Rudzicz, Frank. - : arXiv, 2020 BASE Show details 3 Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power ... Novikova, Jekaterina; Balagopalan, Aparna; Shkaruta, Ksenia. - : arXiv, 2019 BASE Show details 4 The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech ... Balagopalan, Aparna; Novikova, Jekaterina; Rudzicz, Frank; Ghassemi, Marzyeh. - : arXiv, 2018 Abstract: Speech datasets for identifying Alzheimer's disease (AD) are generally restricted to participants performing a single task, e.g. describing an image shown to them. As a result, models trained on linguistic features derived from such datasets may not be generalizable across tasks. Building on prior work demonstrating that same-task data of healthy participants helps improve AD detection on a single-task dataset of pathological speech, we augment an AD-specific dataset consisting of subjects describing a picture with multi-task healthy data. We demonstrate that normative data from multiple speech-based tasks helps improve AD detection by up to 9%. Visualization of decision boundaries reveals that models trained on a combination of structured picture descriptions and unstructured conversational speech have the least out-of-task error and show the most potential to generalize to multiple tasks. We analyze the impact of age of the added samples and if they affect fairness in classification. We also provide ... : Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216 ... Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Machine Learning stat.ML; Sound cs.SD URL: https://dx.doi.org/10.48550/arxiv.1811.12254 https://arxiv.org/abs/1811.12254 BASE Hide details 5 Detecting cognitive impairments by agreeing on interpretations of linguistic features ... Zhu, Zining; Novikova, Jekaterina; Rudzicz, Frank. - : arXiv, 2018 BASE Show details 6 Deconfounding age effects with fair representation learning when assessing dementia ... Zhu, Zining; Novikova, Jekaterina; Rudzicz, Frank. - : arXiv, 2018 BASE Show details

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