Machine learning based estimation of hoarseness severity using sustained vowelsa).
J Acoust Soc Am
; 155(1): 381-395, 2024 01 01.
Article
de En
| MEDLINE
| ID: mdl-38240668
ABSTRACT
Auditory perceptual evaluation is considered the gold standard for assessing voice quality, but its reliability is limited due to inter-rater variability and coarse rating scales. This study investigates a continuous, objective approach to evaluate hoarseness severity combining machine learning (ML) and sustained phonation. For this purpose, 635 acoustic recordings of the sustained vowel /a/ and subjective ratings based on the roughness, breathiness, and hoarseness scale were collected from 595 subjects. A total of 50 temporal, spectral, and cepstral features were extracted from each recording and used to identify suitable ML algorithms. Using variance and correlation analysis followed by backward elimination, a subset of relevant features was selected. Recordings were classified into two levels of hoarseness, H<2 and H≥2, yielding a continuous probability score y∈[0,1]. An accuracy of 0.867 and a correlation of 0.805 between the model's predictions and subjective ratings was obtained using only five acoustic features and logistic regression (LR). Further examination of recordings pre- and post-treatment revealed high qualitative agreement with the change in subjectively determined hoarseness levels. Quantitatively, a moderate correlation of 0.567 was obtained. This quantitative approach to hoarseness severity estimation shows promising results and potential for improving the assessment of voice quality.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Enrouement
/
Dysphonie
Type d'étude:
Diagnostic_studies
/
Prognostic_studies
/
Qualitative_research
Limites:
Humans
Langue:
En
Journal:
J Acoust Soc Am
Année:
2024
Type de document:
Article
Pays d'affiliation:
Allemagne