Objective This work explores the advances in conversational agents aimed at the detection
of mental health disorders, and specifically the screening of depression. The focus is put on …

Internet-connected devices, such as smartphones, smartwatches, and laptops, have become
ubiquitous in modern life, reaching ever deeper into our private spheres. Among the sensors …

Mental health disorders—including depression, anxiety, trauma-related, and psychotic
conditions—are pervasive and impairing, representing considerable challenges for both …

Depression detection from speech continues to attract significant research attention but
remains a major challenge, particularly when the speech is acquired from diverse …

The massive and growing burden imposed on modern society by depression has motivated
investigations into early detection through automated, scalable and non-invasive methods …

Background Despite growing efforts to develop user-friendly artificial intelligence (AI)
applications for clinical care, their adoption remains limited because of the barriers at …

Background Vocal biomarker–based machine learning approaches have shown promising
results in the detection of various health conditions, including respiratory diseases, such as …

Mental illnesses are often undiagnosed, highlighting the need for an effective alternative to
traditional screening surveys. We propose our Early Mental Health Uncovering (EMU) …

The processing of speech as an explicit sequence of events is common in automatic speech
recognition (linguistic events), but has received relatively little attention in paralinguistic …

Depression disorders are a major growing concern worldwide, especially given the unmet
need for widely deployable depression screening for use in real-world environments …