This paper is a structured methodological review of acoustic parameters for characterizing vocal tremor, comparing definitions, computations, and implementations across common voice-analysis software and advocating a multidimensional parameter framework.

Standardizing and clarifying voice-acoustic features can improve development and reliability of speech-based biomarkers for Parkinson's disease diagnosis and monitoring, aiding patient stratification and outcome measurement in trials, though it offers little direct insight into therapeutic…

UNLABELLED: Vocal tremor is a relevant feature in several neurological voice disorders, including essential vocal tremor and Parkinson's disease. However, its acoustic characterization remains challenging due to the complex and often nonstationary nature of voice signals. In this study, we present a structured methodological review of acoustic parameters used in voice analysis, with particular emphasis on vocal tremor, aiming to organize and clarify their conceptual interpretation, computational formulation, and implementation in widely adopted voice analysis software. Illustrative examples based on sustained vowel samples from healthy and pathological voices are included solely to exemplify the behavior of selected parameters and to support the methodological discussion, rather than to constitute the primary focus of the study. METHOD: This study adopts a structured methodological review approach. Relevant literature on voice acoustic analysis, vocal tremor, and Parkinson's disease was surveyed, focusing on studies that define, compute, and apply acoustic parameters for sustained vowel analysis. Acoustic features were selected based on their recurrent use in the literature, clinical relevance to neurological voice disorders, and clarity of conceptual and mathematical definition, encompassing conventional perturbation and noise measures, nonlinear parameters, and a dedicated set of vocal tremor features. The computational formulations of these parameters were systematically analyzed and contrasted across commonly used voice analysis software packages, including Multi-Dimensional Voice Program, Praat, and VoxMetria. A descriptive and comparative analysis was conducted to identify conceptual ambiguities, differences in implementation, and practical implications across parameters and software tools. Illustrative examples from healthy and pathological voices were included solely to support the methodological discussion. RESULTS: The comparative and descriptive analysis revealed substantial variability in the conceptual definitions and computational formulations of acoustic parameters across the reviewed literature and voice analysis software. Differences were observed in parameter naming, mathematical implementation, and underlying signal assumptions, particularly for vocal tremor-related features. The analysis also highlighted inconsistencies in the availability and calculation of tremor parameters across commonly used software tools. Illustrative vowel examples demonstrated distinct behaviors among parameter groups when applied to healthy and pathological voices. CONCLUSIONS: This work emphasizes that acoustic characterization of vocal tremor benefits from a multidimensional framework that integrates traditional, tremor-specific, and nonlinear parameters. By clarifying conceptual definitions and computational aspects, the study provides a reference-oriented perspective that supports consistent interpretation of results and informed application of acoustic parameters in both research and clinical contexts.