@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{joudeh_predicting_2024,

title = {Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features †},

author = {I. O. Joudeh and A. -M. Cretu and S. Bouchard},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198382238&doi=10.3390%2fs24134398&partnerID=40&md5=cefa8b2e2c044d02f99662af350007db},

doi = {10.3390/s24134398},

issn = {14248220 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Sensors},

volume = {24},

number = {13},

publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},

abstract = {The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA’s video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson’s correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions. © 2024 by the authors.},

note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},

keywords = {adult, Affective interaction, Arousal, artificial neural network, Cognitive state, Cognitive/emotional state, Collaborative interaction, computer, Convolutional neural networks, correlation coefficient, Deep learning, emotion, Emotional state, Emotions, female, Forecasting, Helmet mounted displays, human, Humans, Learning algorithms, Learning systems, Long short-term memory, Machine learning, Machine-learning, male, Mean square error, Neural networks, physiology, Regression, Root mean squared errors, Video recording, virtual reality, Visual feature, visual features},

pubstate = {published},

tppubtype = {article}

}

@article{joudehPredictingArousalValence2024,

title = {Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features †},

author = {I. O. Joudeh and A. -M. Cretu and S. Bouchard},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198382238&doi=10.3390%2fs24134398&partnerID=40&md5=cefa8b2e2c044d02f99662af350007db},

doi = {10.3390/s24134398},

issn = {14248220 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Sensors},

volume = {24},

number = {13},

publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},

abstract = {The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA’s video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson’s correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions. © 2024 by the authors.},

keywords = {adult, Affective interaction, Arousal, artificial neural network, Cognitive state, Cognitive/emotional state, Collaborative interaction, computer, Convolutional neural networks, correlation coefficient, Deep learning, emotion, Emotional state, Emotions, female, Forecasting, Helmet mounted displays, human, Humans, Learning algorithms, Learning systems, Long short-term memory, Machine learning, Machine-learning, male, Mean square error, Neural networks, physiology, Regression, Root mean squared errors, Video recording, virtual reality, Visual feature, visual features},

pubstate = {published},

tppubtype = {article}

}