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Review
. 2024 Nov 11;16(11):e73491.
doi: 10.7759/cureus.73491. eCollection 2024 Nov.

The Rapidly Evolving Scenario of Acoustic Voice Analysis in Otolaryngology

Marco Fantini  1   2 , Gabriele Ciravegna  3 , Alkis Koudounas  3 , Tania Cerquitelli  3 , Elena Baralis  3 , Giovanni Succo  4   5 , Erika Crosetti  4
Affiliations
Review

The Rapidly Evolving Scenario of Acoustic Voice Analysis in Otolaryngology

Marco Fantini et al. Cureus. .

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.

Keywords: acoustic analysis; acoustic voice quality index; artificial intelligence; vocal quality; voice.

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Conflict of interest statement

Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1. Graphic representation of a cepstrum.
This figure was obtained using the free software PRAAT (www.fon.hum.uva.nl/praat/) by the author Marco Fantini.
Figure 2
Figure 2. Example of the Acoustic Voice Quality Index (AVQI), version 03.01, with the Italian language cut-off 2.35.
This figure was obtained using the free software PRAAT (www.fon.hum.uva.nl/praat/) by the author Marco Fantini.
See this image and copyright information in PMC

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