Feature Analysis of the Vowel [a:] in Individuals With Chronic Obstructive Pulmonary Disease and Healthy Controls
Idrisoglu, Alper ; Moraes, Ana Luiza Dallora ; Cheddad, Abbas ; Anderberg, Peter LU ; Whitling, Susanna LU ; Jakobsson, Andreas LU
and Berglund, Johan Sanmartin
(2025)
In Journal of Voice
- Abstract
Background: In addition to impairing the lung function, chronic obstructive pulmonary disease (COPD) also affects phonatory characteristics. Recent research highlights the potential of voice as a digital biomarker to support clinical decision-making. While machine learning (ML) can detect disease patterns from acoustic features, clinical relevance requires understanding the relationship between the disorder and acoustic features. Objective: This study investigates both statistical and clinical significance using Baseline Acoustic (BLA) and Mel-Frequency Cepstral Coefficient (MFCC) features with focusing on individuals with COPD and healthy controls (HC). Method: Acoustic features derived from Swedish utterances of the vowel [a:],... (More)
Background: In addition to impairing the lung function, chronic obstructive pulmonary disease (COPD) also affects phonatory characteristics. Recent research highlights the potential of voice as a digital biomarker to support clinical decision-making. While machine learning (ML) can detect disease patterns from acoustic features, clinical relevance requires understanding the relationship between the disorder and acoustic features. Objective: This study investigates both statistical and clinical significance using Baseline Acoustic (BLA) and Mel-Frequency Cepstral Coefficient (MFCC) features with focusing on individuals with COPD and healthy controls (HC). Method: Acoustic features derived from Swedish utterances of the vowel [a:], recorded via mobile phones from 48 age-matched participants (24 COPD, 24 HC; equal gender distribution), were analyzed. To reduce bias from varying recording counts, features were aggregated by averaging 10 randomly selected recordings per participant over 100 iterations. Vowel articulation was visualized in the vowel quadrilateral space using F1 (tongue height) and F2 (tongue advancement). Group differences were assessed using the Shapiro-Wilk test, Mann-Whitney U test (α = 0.05), Benjamini-Hochberg (BH) and Bonferroni corrections, Permutational Multivariate Analysis of Variance (PERMANOVA) test, and Cliff's Delta (δ). Results: Of 101 features, 29 remained significant after BH correction and one after Bonferroni. Multivariate testing (p = 0.019) showed group separation. Additionally, 34 features demonstrated large effect sizes, suggesting potential as digital biomarkers. Conclusion: Voice data recorded via mobile phones capture meaningful acoustic differences associated with COPD. These findings support the integration of voice-based assessments into eHealth platforms for noninvasive COPD screening and monitoring, which is pending further validation on larger populations.
(Less)
- author
- Idrisoglu, Alper
; Moraes, Ana Luiza Dallora
; Cheddad, Abbas
; Anderberg, Peter
LU
; Whitling, Susanna
LU
; Jakobsson, Andreas
LU
and Berglund, Johan Sanmartin
- organization
-
- The voice group (research group)
- Communication and Cognition (research group)
- Birgit Rausing Centre for Medical Humanities (BRCMH)
- Mathematical Statistics
- eSSENCE: The e-Science Collaboration
- LTH Profile Area: Engineering Health
- LTH Profile Area: AI and Digitalization
- LU Profile Area: Natural and Artificial Cognition
- ELLIIT: the Linköping-Lund initiative on IT and mobile communication
- publishing date
- 2025
- type
- Contribution to journal
- publication status
- epub
- subject
- keywords
- Chronic obstructive pulmonary disease, Effect size, Mel-frequency cepstral coefficient, Mobile phone-recorded voice data, Statistical analysis, Voice features
- in
- Journal of Voice
- publisher
- Elsevier
- external identifiers
-
- scopus:105024714181
- pmid:41168019
- ISSN
- 0892-1997
- DOI
- 10.1016/j.jvoice.2025.10.013
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: The Authors
- id
- 4774ca66-07a3-42b5-b56a-bd5e80a07535
- date added to LUP
- 2026-03-02 15:04:06
- date last changed
- 2026-04-13 23:16:33
@article{4774ca66-07a3-42b5-b56a-bd5e80a07535,
abstract = {{<p>Background: In addition to impairing the lung function, chronic obstructive pulmonary disease (COPD) also affects phonatory characteristics. Recent research highlights the potential of voice as a digital biomarker to support clinical decision-making. While machine learning (ML) can detect disease patterns from acoustic features, clinical relevance requires understanding the relationship between the disorder and acoustic features. Objective: This study investigates both statistical and clinical significance using Baseline Acoustic (BLA) and Mel-Frequency Cepstral Coefficient (MFCC) features with focusing on individuals with COPD and healthy controls (HC). Method: Acoustic features derived from Swedish utterances of the vowel [a:], recorded via mobile phones from 48 age-matched participants (24 COPD, 24 HC; equal gender distribution), were analyzed. To reduce bias from varying recording counts, features were aggregated by averaging 10 randomly selected recordings per participant over 100 iterations. Vowel articulation was visualized in the vowel quadrilateral space using F1 (tongue height) and F2 (tongue advancement). Group differences were assessed using the Shapiro-Wilk test, Mann-Whitney U test (α = 0.05), Benjamini-Hochberg (BH) and Bonferroni corrections, Permutational Multivariate Analysis of Variance (PERMANOVA) test, and Cliff's Delta (δ). Results: Of 101 features, 29 remained significant after BH correction and one after Bonferroni. Multivariate testing (p = 0.019) showed group separation. Additionally, 34 features demonstrated large effect sizes, suggesting potential as digital biomarkers. Conclusion: Voice data recorded via mobile phones capture meaningful acoustic differences associated with COPD. These findings support the integration of voice-based assessments into eHealth platforms for noninvasive COPD screening and monitoring, which is pending further validation on larger populations.</p>}},
author = {{Idrisoglu, Alper and Moraes, Ana Luiza Dallora and Cheddad, Abbas and Anderberg, Peter and Whitling, Susanna and Jakobsson, Andreas and Berglund, Johan Sanmartin}},
issn = {{0892-1997}},
keywords = {{Chronic obstructive pulmonary disease; Effect size; Mel-frequency cepstral coefficient; Mobile phone-recorded voice data; Statistical analysis; Voice features}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Journal of Voice}},
title = {{Feature Analysis of the Vowel [a:] in Individuals With Chronic Obstructive Pulmonary Disease and Healthy Controls}},
url = {{http://dx.doi.org/10.1016/j.jvoice.2025.10.013}},
doi = {{10.1016/j.jvoice.2025.10.013}},
year = {{2025}},
}