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Evaluation of an AI-Based Voice Biomarker Tool to Detect Signals Consistent With Moderate to Severe Depression.
Ann Fam Med
January 2025
Departments of Psychiatry and Emergency Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
Purpose: Mental health screening is recommended by the US Preventive Services Task Force for all patients in areas where treatment options are available. Still, it is estimated that only 4% of primary care patients are screened for depression. The goal of this study was to evaluate the efficacy of machine learning technology (Kintsugi Voice, v1, Kintsugi Mindful Wellness, Inc) to detect and analyze voice biomarkers consistent with moderate to severe depression, potentially allowing for greater compliance with this critical primary care public health need.
View Article and Find Full Text PDFA multi-level feature fusion artificial neural network for classification of acoustic emission signals.
Ann N Y Acad Sci
January 2025
Hainan Institute, Zhejiang University, Sanya, China.
In this paper, we introduce FUSION-ANN, a novel artificial neural network (ANN) designed for acoustic emission (AE) signal classification. FUSION-ANN comprises four distinct ANN branches, each housing an independent multilayer perceptron. We extract denoised features of speech recognition such as linear predictive coding, Mel-frequency cepstral coefficient, and gammatone cepstral coefficient to represent AE signals.
View Article and Find Full Text PDFEffects of Recording Condition and Number of Monitored Days on the Discriminative Power of the Daily Phonotrauma Index.
J Speech Lang Hear Res
January 2025
Center for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston.
Purpose: The Daily Phonotrauma Index (DPI) can quantify pathophysiological mechanisms associated with daily voice use in individuals with phonotraumatic vocal hyperfunction (PVH). Since DPI was developed based on weeklong ambulatory voice monitoring, this study investigated if DPI can achieve comparable performance using (a) short laboratory speech tasks and (b) fewer than 7 days of ambulatory data.
Method: An ambulatory voice monitoring system recorded the vocal function/behavior of 134 females with PVH and vocally healthy matched controls in two different conditions.
Clinical Decision Support Using Speech Signal Analysis: Systematic Scoping Review of Neurological Disorders.
J Med Internet Res
January 2025
Knight Foundation of Computing & Information Sciences, Florida International University, Miami, FL, United States.
Background: Digital biomarkers are increasingly used in clinical decision support for various health conditions. Speech features as digital biomarkers can offer insights into underlying physiological processes due to the complexity of speech production. This process involves respiration, phonation, articulation, and resonance, all of which rely on specific motor systems for the preparation and execution of speech.
View Article and Find Full Text PDFRespiratory muscle training in people with cervical spinal cord injury - A systematic review.
J Spinal Cord Med
January 2025
Speech-Language-Hearing Sciences, Medical School, Federal University of Minas Gerais, Belo Horizonte, Brazil.
Introduction: Spinal cord injury is a physiological disruption often caused by trauma, leading to severe physical and psychological effects, including irreversible impairment and disability. Cervical injuries, particularly between C1 and C8, are the most severe, potentially causing diaphragm paralysis and requiring mechanical ventilation. Reduced respiratory muscle strength not only affects respiratory function but also significantly impacts voice, speech, and communication, which are crucial for quality of life.
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