@article{zhuWavRxDiseaseAgnosticGeneralizable2025,

title = {WavRx: A Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model},

author = {Y. Zhu and T. Falk},

url = {https://www.scopus.com/pages/publications/85203439930?origin=resultslist},

doi = {10.1109/JBHI.2024.3454550},

issn = {21682194 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {29},

number = {9},

pages = {6353–6365},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Speech is known to carry health-related attributes, which has emerged as a novel venue for remote and long-term health monitoring. However, existing models are usually tailored for a specific type of disease, and have been shown to lack generalizability across datasets. Furthermore, concerns have been raised recently towards the leakage of speaker identity from health embeddings. To mitigate these limitations, we propose WavRx, a speech health diagnostics model that captures the respiration and articulation related dynamics from a universal speech representation. Our in-domain and cross-domain experiments on six pathological speech datasets demonstrate WavRx as a new state-of-the-art health diagnostic model. Furthermore, we show that the amount of speaker identity entailed in the WavRx health embeddings is significantly reduced without extra guidance during training. An in-depth analysis of the model was performed, thus providing physiological interpretation of its improved generalizability and privacy-preserving ability. © 2013 IEEE.},

keywords = {Agnostic, area under the curve, article, artificial neural network, asthma, autoencoder, Benchmarking, breathing, chronic obstructive lung disease, Computer-Assisted, controlled study, convolutional neural network, coronavirus disease 2019, Cross-domain, Databases, Diagnosis, Diagnostic, Diagnostic model, diagnostic test accuracy study, diagnostics, Differential privacy, Dynamics, dysarthria, Electronic health record, embedding, Embeddings, Factual, factual database, Generalizability, Health embedding, Health embeddings, Health monitoring, human, Humans, Machine learning, malignant neoplasm, model, Pathological speech, pathophysiology, physiology, pneumonia, Privacy, Privacy preserving, privacy preserving speech health diagnostic model, privacy-preserving, Privacy-preserving techniques, receiver operating characteristic, short time Fourier transform, Signal processing, speech, speech articulation, speech disorder, Speech Disorders, State of the art, temporal representation encoder, training, waveform},

pubstate = {published},

tppubtype = {article}

}

@article{ouyed_feature_2018,

title = {Feature weighting for multinomial kernel logistic regression and application to action recognition},

author = {O. Ouyed and M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035104467&doi=10.1016%2fj.neucom.2017.10.024&partnerID=40&md5=09687b392a405be4338799a750932cf3},

doi = {10.1016/j.neucom.2017.10.024},

issn = {09252312},

year  = {2018},

date = {2018-01-01},

journal = {Neurocomputing},

volume = {275},

pages = {1752–1768},

publisher = {Elsevier B.V.},

abstract = {Multinominal kernel logistic regression (MKLR) is a supervised classification method designed for separating classes with non-linear boundaries. However, it relies on the assumption that all features are equally important, which may decrease classification performance when dealing with high-dimensional and noisy data. We propose an approach for embedding feature relevance in multinomial kernel logistic regression. Our approach, coined fr-MKLR, generalizes MKLR by introducing a feature weighting scheme in the Gaussian kernel and using the so-called ℓ0-“norm” as sparsity-promoting regularization. Therefore, the contribution of each feature is tuned according to its relevance for classification which leads to more generalizable and interpretable sparse models for classification. Application of our approach to several standard datasets and video action recognition has provided very promising results compared to other methods. © 2017 Elsevier B.V.},

note = {Publisher: Elsevier B.V.},

keywords = {Action recognition, article, Classification, classification algorithm, Classification performance, Computer applications, controlled study, embedding, Feature relevance, feature relevance for multinomial kernel logistic regression, Feature weighting, Kernel logistic regression, kernel method, Learning, mathematical computing, Multinomial kernels, multinominal kernel logistic regression, Neural networks, priority journal, recognition, regression analysis, simulation, sparse modeling, Sparse models, sparse multinomial logistic regression, sparsity promoting regularization, standard, Supervised classification},

pubstate = {published},

tppubtype = {article}

}

@article{ouyedFeatureWeightingMultinomial2018,

title = {Feature weighting for multinomial kernel logistic regression and application to action recognition},

author = {O. Ouyed and M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035104467&doi=10.1016%2fj.neucom.2017.10.024&partnerID=40&md5=09687b392a405be4338799a750932cf3},

doi = {10.1016/j.neucom.2017.10.024},

issn = {09252312},

year  = {2018},

date = {2018-01-01},

journal = {Neurocomputing},

volume = {275},

pages = {1752–1768},

publisher = {Elsevier B.V.},

abstract = {Multinominal kernel logistic regression (MKLR) is a supervised classification method designed for separating classes with non-linear boundaries. However, it relies on the assumption that all features are equally important, which may decrease classification performance when dealing with high-dimensional and noisy data. We propose an approach for embedding feature relevance in multinomial kernel logistic regression. Our approach, coined fr-MKLR, generalizes MKLR by introducing a feature weighting scheme in the Gaussian kernel and using the so-called ℓ0-“norm” as sparsity-promoting regularization. Therefore, the contribution of each feature is tuned according to its relevance for classification which leads to more generalizable and interpretable sparse models for classification. Application of our approach to several standard datasets and video action recognition has provided very promising results compared to other methods. © 2017 Elsevier B.V.},

keywords = {Action recognition, article, Classification, classification algorithm, Classification performance, Computer applications, controlled study, embedding, Feature relevance, feature relevance for multinomial kernel logistic regression, Feature weighting, Kernel logistic regression, kernel method, Learning, mathematical computing, Multinomial kernels, multinominal kernel logistic regression, Neural networks, priority journal, recognition, regression analysis, simulation, sparse modeling, Sparse models, sparse multinomial logistic regression, sparsity promoting regularization, standard, Supervised classification},

pubstate = {published},

tppubtype = {article}

}