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 …

Objective This study aims to conduct a systematic review and meta-analysis of the
diagnostic accuracy of deep learning (DL) using speech samples in depression. Materials …

Multimodal depression detection (MDD) has garnered significant interest in recent years.
Current methods typically integrate multimodal information within samples to distinguish …

Depression, a prevalent mental health disorder impacting millions globally, demands
reliable assessment systems. Unlike previous studies that focus solely on either detecting …

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 recordings are being more frequently used to detect and monitor disease, leading to
privacy concerns. Beyond cryptography, protection of speech can be addressed by …

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 …

Depression, as a prevalent and severe psychological disorder, has become a burden to
individuals, families and societies all over the world. Recently, some deep learning methods …

Abstract Major Depressive Disorder (MDD) is a prevalent mental illness that affects a
significant portion of the global population. Despite its severity, traditional diagnostic …