相关论文: The Vocal Signature of Social Anxiety: Exploration…
Suicide remains a public health challenge, necessitating improved detection methods to facilitate timely intervention and treatment. This systematic review evaluates the role of Artificial Intelligence (AI) and Machine Learning (ML) in…
Individuals high in social anxiety symptoms often exhibit elevated state anxiety in social situations. Research has shown it is possible to detect state anxiety by leveraging digital biomarkers and machine learning techniques. However, most…
Social Anxiety Disorder (SAD) significantly impacts individuals' daily lives and relationships. The conventional methods for SAD detection involve physical consultations and self-reported questionnaires, but they have limitations such as…
Anxiety affects hundreds of millions of individuals globally, yet large-scale screening remains limited. Social media language provides an opportunity for scalable detection, but current models often lack interpretability,…
More and more people are experiencing pressure from work, life, and education. These pressures often lead to an anxious state of mind, or even the early symptoms of suicidal ideation. With the advancement of artificial intelligence (AI)…
The prevalence of suicide has been on the rise since the 20th century, causing severe emotional damage to individuals, families, and communities alike. Despite the severity of this suicide epidemic, there is so far no reliable and…
In emergency medicine, timely intervention for patients at risk of suicide is often hindered by delayed access to specialised psychiatric care. To bridge this gap, we introduce a speech-based approach for automatic suicide risk assessment.…
Barriers to accessing mental health assessments including cost and stigma continues to be an impediment in mental health diagnosis and treatment. Machine learning approaches based on speech samples could help in this direction. In this…
Gender-based violence is a pervasive public health issue that severely impacts women's mental health, often leading to conditions such as in anxiety, depression, post-traumatic stress disorder, and substance abuse. Identifying the…
Conventional methods for diagnosing Social Anxiety Disorder (SAD), such as clinical interviews and self-reported questionnaires, often face accessibility barriers and subjective biases, underscoring the need for objective physiological…
Speech and language technologies offer valuable opportunities for supporting mental health assessment through objective and interpretable cues. We present a systematic feature-based analysis framework leveraging perceptually grounded…
Disorders of voice production have severe effects on the quality of life of the affected individuals. A simulation approach is used to investigate the cause-effect chain in voice production showing typical characteristics of voice such as…
Objectives: ncreased prevalence of social creak particularly among female speakers has been reported in several studies. The study of social creak has been previously conducted by combining perceptual evaluation of speech with conventional…
Diagnosing autism spectrum disorder (ASD) by identifying abnormal speech patterns from examiner-patient dialogues presents significant challenges due to the subtle and diverse manifestations of speech-related symptoms in affected…
Automated speaking assessment (ASA) typically involves automatic speech recognition (ASR) and hand-crafted feature extraction from the ASR transcript of a learner's speech. Recently, self-supervised learning (SSL) has shown stellar…
Traditional screening practices for anxiety and depression pose an impediment to monitoring and treating these conditions effectively. However, recent advances in NLP and speech modelling allow textual, acoustic, and hand-crafted…
Usually, people talk neutrally in environments where there are no abnormal talking conditions such as stress and emotion. Other emotional conditions that might affect people talking tone like happiness, anger, and sadness. Such emotions are…
Early identification of suicide risk is crucial for preventing suicidal behaviors. As a result, the identification and study of patterns and markers related to suicide risk have become a key focus of current research. In this paper, we…
Depression commonly co-occurs with neurodegenerative disorders like Multiple Sclerosis (MS), yet the potential of speech-based Artificial Intelligence for detecting depression in such contexts remains unexplored. This study examines the…
Automated speaking assessment (ASA) on opinion expressions is often hampered by the scarcity of labeled recordings, which restricts prompt diversity and undermines scoring reliability. To address this challenge, we propose a novel training…