Acoustic analysis in stuttering: a machine-learning study.

Background: Stuttering is a childhood-onset neurodevelopmental disorder affecting speech fluency. The diagnosis and clinical management of stuttering is currently based on perceptual examination and clinical scales. Standardized techniques for acoustic analysis have prompted promising results for the objective assessment of dysfluency in people with stuttering (PWS).

Artificial Intelligence-Based Voice Assessment of Patients with Parkinson's Disease Off and On Treatment: Machine vs. Deep-Learning Comparison.

Parkinson's Disease (PD) is one of the most common non-curable neurodegenerative diseases. Diagnosis is achieved clinically on the basis of different symptoms with considerable delays from the onset of neurodegenerative processes in the central nervous system. In this study, we investigated early and full-blown PD patients based on the analysis of their voice characteristics with the aid of the most commonly employed machine learning (ML) techniques.

Voice in Parkinson's Disease: A Machine Learning Study.

Introduction: Parkinson's disease (PD) is characterized by specific voice disorders collectively termed hypokinetic dysarthria. We here investigated voice changes by using machine learning algorithms, in a large cohort of patients with PD in different stages of the disease, OFF and ON therapy.

Methods: We investigated 115 patients affected by PD (mean age: 68.

Voice Analysis with Machine Learning: One Step Closer to an Objective Diagnosis of Essential Tremor.

Background: Patients with essential tremor have upper limb postural and action tremor often associated with voice tremor. The objective of this study was to objectively examine voice tremor and its response to symptomatic pharmacological treatment in patients with essential tremor using voice analysis consisting of power spectral analysis and machine learning.

Methods: We investigated 58 patients (24 men; mean age ± SD, 71.

Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender.

Background: Experimental studies using qualitative or quantitative analysis have demonstrated that the human voice progressively worsens with ageing. These studies, however, have mostly focused on specific voice features without examining their dynamic interaction. To examine the complexity of age-related changes in voice, more advanced techniques based on machine learning have been recently applied to voice recordings but only in a laboratory setting.