Related Experiment Video
Updated: May 28, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
Published on: August 9, 2024
Discovering Hidden Vocal Subtypes: An Unsupervised Acoustic-Biomechanical Exploration of Voice Profiles
Margarita Pérez-Bonilla1, Paola Díaz-Borrego2, Marina Mora-Ortiz3
1Physical Medicine & Rehabilitation, Reina Sofía University Hospital, 14004 Córdoba, Spain; Department of Applied Physics, Radiology and Physical Medicine, Faculty of Medicine and Nursing of Córdoba, 14004 Córdoba, Spain.
Objective:
This study aims to explore latent acoustic-biomechanical patterns of voice production using an unsupervised multivariate approach, and to identify data-driven vocal profiles across individuals with amyotrophic lateral sclerosis (ALS) and nonneurological dysphonia.
Methods:
A cross-sectional sample of 100 individuals, including patients with ALS and individuals with nonneurological dysphonia, was analyzed. Sustained vowel phonation was recorded and characterized using 26 variables, including standard acoustic measures (fundamental frequency -fo-, jitter, shimmer, and harmonics-to-noise ratio (HNR)) and 22 biomechanical parameters. Principal component analysis was applied to investigate relationships among variables and reduce dimensionality. Unsupervised clustering was performed at both the variable level to identify functional groupings and the participant level to derive data-driven voice profiles. Cluster validity was assessed using internal indices. Post hoc statistical comparisons and chi-square tests were used descriptively to characterize between-cluster differences and their relationship with clinical categories.
Results:
The first five principal components explained 70.7% of the total variance, revealing structured relationships between acoustic and biomechanical features. Participant level clustering consistently supported a two-profile solution. Fifteen voice parameters differed significantly between profiles after false discovery rate correction, with the largest effects observed for shimmer, HNR, and the biomechanical parameter Pr11, reflecting differences in vocal stability and noise-related characteristics. The identified profiles were not significantly associated with clinical diagnostic categories.
Conclusions:
An unsupervised multimodal analysis of sustained phonation revealed two coherent vocal profiles that transcend traditional diagnostic labels. These data-driven voice phenotypes may capture functional patterns of voice production and support future efforts toward more refined and personalized characterization of voice disorders.
Related Concept Videos
Perceiving Loudness, Pitch, and Location
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by identifying...
Assessment of Ventilation II: Respiratory Depth and Rhythm
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
Facial Feedback Hypothesis
Auditory Perception
Larynx
Anatomy of the Larynx
The larynx consists of various components, including cartilage, muscles, and vocal cords. Its structure includes three large unpaired cartilages—the thyroid, cricoid, and epiglottis—and three smaller paired cartilages—the arytenoids, corniculates, and...
Respiratory System Abnormal Finding II: Palpation and Auscultation
Palpation Findings
During a respiratory assessment, palpation can reveal several vital abnormalities:

