arXiv AI June 6, 2026 By Xueyang Wu, Siyuan Liu, Kezhuo Yang, Guang Ling

InfoShield: Privacy-Preserving Speech Representations for Mental Health Screening via Information-Theoretic Optimization

arXiv:2606. 05561v1 Announce Type: cross Abstract: Speech-based mental health screening offers scalable depression detection, yet clinical deployment faces a significant barrier: users' privacy concerns about demographic information exposure.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Aug 10

LSEAD: A Privacy-Preserving LLM-Based Speech Analysis Framework for Early Alzheimer's Disease Screening

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.

By Xin Wang, Yingchao Huang, Yuhan Su, Shanshan Yao, Wei Peng

llms rag benchmarks safety

arXiv Machine Learning

Jul 24

Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models

arXiv:2607. 21496v1 Announce Type: cross Abstract: Cognitive impairment (CI) is a growing public health concern.

By Yingchao Huang, Xin Wang, Yuhan Su, Shanshan Yao

llms rag multimodal benchmarks safety

arXiv AI

Jul 28

Multimodal Domain Generalization for Depression Detection: An Attention-Based BiLSTM Network with Domain-Adversarial Training

arXiv:2607. 22794v1 Announce Type: cross Abstract: Automatic depression detection with deep learning has shown promise but often suffers from limited generalization due to domain shift arising from inter-speaker variability.

By Ali Tabaraei, Federico Simonetta, Stavros Ntalampiras

multimodal benchmarks safety

arXiv AI

Jul 29

Depression Markers in Speech: An Approach based on Tract Variables Dynamics

arXiv:2607. 25888v1 Announce Type: cross Abstract: This study identifies new depression biomarkers based on the dynamical properties of tract variables, which represent geometric features describing the configuration of the speech articulators.

By Sahar Altalhi, Tanaya Guha, Alessandro Vinciarelli

arXiv Machine Learning

Jun 18

Fair Cognitive Impairment Detection Through Unlearning

arXiv:2606. 18571v1 Announce Type: new Abstract: Mild Cognitive Impairment (MCI) is a medical condition characterized by a noticeable decline in memory, language, or thinking abilities.

By William Nguyen, Jiali Cheng, Hadi Amiri

rag multimodal benchmarks

arXiv AI

Jul 7

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives

arXiv:2607. 03744v1 Announce Type: new Abstract: Automatic depression detection from clinical interviews typically models the semantic content and acoustic characteristics of participant speech.

By Hanie Kang, Huang-Cheng Chou, Sudarsana Reddy Kadiri, Shrikanth Narayanan

Related stories

LSEAD: A Privacy-Preserving LLM-Based Speech Analysis Framework for Early Alzheimer's Disease Screening

Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models

Multimodal Domain Generalization for Depression Detection: An Attention-Based BiLSTM Network with Domain-Adversarial Training

Depression Markers in Speech: An Approach based on Tract Variables Dynamics

Fair Cognitive Impairment Detection Through Unlearning

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives