Authors
Zhenyi Hou, Tianhao Zhang, Shanggerile Jiang, Xinyu Sheng, Xu Zhao, Kejie Ye, Kaili Geng, Yan Zou, Wei Yuan, Weining Pi, Yue Fan, Xin Yuan, Guangyu Fan
Published in
Journal of voice : official journal of the Voice Foundation. Oct 23, 2026. Epub Oct 23, 2026.
Abstract
Key concepts in classical vocal technique, including laryngeal-position stability, resonance, and vocal openness, are central to voice teaching but remain largely dependent on auditory judgment. Their relationships with reproducible acoustic patterns remain insufficiently understood.
This study investigated whether interpretable acoustic features could be associated with expert ratings of these three pedagogical dimensions using machine-learning models and examined acoustic changes following Combined Vocal and Core Muscle Stability (C-VaC) warm-up.
A dataset of 3000 audio samples from 12 trained female singers, including paired recordings before and after C-VaC warm-up, was analyzed. Acoustic features were extracted, and separate support vector machine (SVM) models were developed to predict expert ratings on a 1-5 ordinal scale. Model performance was evaluated using subject-independent cross-validation.
Under 10-fold cross-validation, the models achieved an overall macro-F1 score of 0.74 and an accuracy of 0.72. The most consistent acoustic improvements after warm-up were observed in the middle register, including reduced vocal perturbation and increased cepstral peak prominence.
Multidimensional acoustic features can help model expert-aligned ratings of key concepts in classical vocal pedagogy. This approach may complement traditional auditory evaluation with quantitative information, although further validation in larger and more diverse singer populations is needed.
PMID:
42919718
Bibliographic data and abstract were imported from PubMed on 24 Oct 2026.
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