AIMC Topic: Voice

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Reliability, validity, and correlates of an AI voice emotion recognition app among nurses.

PloS one
BACKGROUND: Digital tools are increasingly widespread in healthcare, particularly in the fields of emotion recognition and mental health assessment.

Earlier prediction of Parkinson's disease using cross non-decimated wavelet transform and machine learning algorithm.

Scientific reports
Parkinson's disease (PD) is a brain disorder, that affects a person's body movement causing stiffness, shaking and imbalance. Earlier detection of PD is a challenging task for researchers. In this paper, earlier detection of PD is performed using the...

Association between voice-activated technology interventions and well-being in older adults living alone: a protocol for a systematic review and meta-analysis.

BMJ open
INTRODUCTION: A growing number of community-dwelling older adults living alone face a range of physical, psychological and social challenges that negatively impact their well-being. Various technologies have been developed to support healthy ageing, ...

Voice as a digital biomarker in schizophrenia: a scoping review protocol on the application of artificial intelligence.

BMJ open
INTRODUCTION: There are many barriers to mental health services, including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited in part by the cyclical nature of psychiatric symptoms. The h...

The role of prompt, voice, and personality factors in the acceptance and evaluation of AI-generated mindfulness exercises.

Scientific reports
AI-generated mindfulness exercises have the potential to provide tailored mindfulness interventions. However, the role of quality of AI-generated mindfulness exercises on their acceptance and evaluation is as of yet underexplored. The present work in...

Voice clones sound realistic but not (yet) hyperrealistic.

PloS one
AI-generated voices are increasingly prevalent in our lives, via virtual assistants, automated customer service, and voice-overs. With increased availability and affordability of AI-generated voices, we need to examine how humans perceive them. Recen...

Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers.

Scientific reports
Traditional diagnostic methods for asthma, a widespread chronic respiratory illness, are often limited by factors such as patient cooperation with spirometry. Non-invasive acoustic analysis using machine learning offers a promising alternative for ob...

Data Collection for Automatic Depression Identification in Spanish Speakers Using Deep Learning Algorithms: Protocol for a Case-Control Study.

JMIR research protocols
BACKGROUND: Depression is a mental health condition that affects millions of people worldwide. Although common, it remains difficult to diagnose due to its heterogeneous symptomatology. Mental health questionnaires are currently the most used assessm...

Voice fatigue subtyping through individual modeling of vocal demand reponses.

Scientific reports
Recognizing individual variability is essential for developing targeted, personalized medical interventions. Vocal fatigue is a prevalent symptom and complaint among occupational voice users, but its identification has yielded mixed results. Vocal fa...

Developing age-specific protocols for pediatric voice databases in artificial intelligence research.

International journal of pediatric otorhinolaryngology
INTRODUCTION: Children's voice and communication abilities evolve with age, necessitating tailored protocols for accurate analysis. Distinct vocal properties and communication styles across developmental stages require specific tasks. The absence of ...