Abstract
The field of voice analysis has experienced significant transformations, evolving from basic perceptual assessments to the incorporation of advanced digital signal processing and computational tools. This progression has facilitated a deeper understanding of the complex dynamics of vocal function, particularly through the use of acoustic voice analysis within a multidimensional evaluation framework. Traditionally, voice analysis relied on parameters such as fundamental frequency, jitter, shimmer, and noise-to-harmonic ratio, which, despite their utility, have faced criticism for variability and lack of robustness. Recent developments have led to a shift toward more reliable metrics such as cepstral measures, which offer improved accuracy in voice quality assessments. Furthermore, the integration of multiparametric constructs underscores a comprehensive approach to evaluating vocal quality, blending sustained vowels, and continuous speech analyses. Current trends in clinical practice increasingly favor these advanced measures over traditional parameters due to their greater reliability and clinical utility. Additionally, the emergence of artificial intelligence (AI), particularly deep learning, holds promise for revolutionizing voice analysis by enhancing diagnostic precision and enabling efficient, non-invasive screening methods. This shift toward AI-driven approaches signifies a potential paradigm change in voice health, suggesting a future where AI not only aids in diagnosis but also the early detection and treatment of voice-related pathologies.