Robust acoustic representation of Japanese vowels under phonation variability using dynamic spectral analysis.

Phonation-induced acoustic variability, particularly in whispered speech, poses persistent challenges for stable vowel representation. Conventional static formant-based descriptors often fail to capture the dynamic spectral behavior of vowels under such conditions. This study evaluates the robustness of acoustic feature representations for Japanese vowels across normal and whispered phonation, comparing static formant measures with dynamic spectral features derived from Vowel Inherent Spectral Change (VISC). Dynamic features are used to characterize continuous formant trajectory patterns, while Support Vector Machine (SVM) and Random Forest (RF) classifiers serve as analytical probes to assess representation stability rather than as targets of algorithmic optimization. Results demonstrate that incorporating dynamic spectral information leads to more consistent vowel discrimination across phonation types compared to static-only representations, with similar performance trends observed across classification frameworks. These findings suggest that differences in classification outcomes are primarily driven by properties of the acoustic representations rather than by model choice. The study highlights the im-portance of representation-level design for robust speech signal analysis under phonation-induced variability and supports the use of low-dimensional dynamic features for vowel modeling in variable phonation conditions.