Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of
improving patient outcomes and optimizing healthcare delivery. By harnessing machine …

Speech-based automatic depression detection systems have been extensively explored
over the past few years. Typically, each speaker is assigned a single label (Depressive or …

Accurately assessing suicide risk is a critical concern in mental health care. Traditional
methods, which often rely on self-reporting and clinical interviews, are limited by their …

Background and objectives Depression is a typical mental illness, and early screening can
effectively prevent exacerbation of the condition. Many studies have found that the …

Speech signals are valuable biomarkers for assessing an individual's mental health,
including identifying Major Depressive Disorder (MDD) automatically. A frequently used …

This study investigates explainable machine learning algorithms for identifying depression
from speech. Grounded in evidence from speech production that depression affects motor …

Detection of depressive symptoms from spoken content has emerged as an efficient Artificial
Intelligence (AI) tool for diagnosing this serious mental health condition. Since speech is a …

Speech-based depression detection poses significant challenges for automated detection
due to its unique manifestation across individuals and data scarcity. Addressing these …

Speaker embeddings are ubiquitous, with applications ranging from speaker recognition
and diarization to speech synthesis and voice anonymization. The amount of information …

The proposed method focuses on speaker disentanglement in the context of depression
detection from speech signals. Previous approaches require patient/speaker labels …