Machine learning based estimation of hoarseness severity using sustained vowelsa)

Schraut T, Schützenberger A, Arias Vergara T, Kunduk M, Echternach M, Döllinger M (2024)

Publication Language: English

Publication Type: Journal article

Publication year: 2024


Book Volume: 155

Pages Range: 381-395

Journal Issue: 1

DOI: 10.1121/10.0024341


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.

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Schraut, T., Schützenberger, A., Arias Vergara, T., Kunduk, M., Echternach, M., & Döllinger, M. (2024). Machine learning based estimation of hoarseness severity using sustained vowelsa). Journal of the Acoustical Society of America, 155(1), 381-395.


Schraut, Tobias, et al. "Machine learning based estimation of hoarseness severity using sustained vowelsa)." Journal of the Acoustical Society of America 155.1 (2024): 381-395.

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