The acoustic voice quality index, version 2.06 and 3.01, for the Persian-speaking population

Zainaee, Shahryar; Khadivi, Ehsan; Jamali, Jamshid; Sobhani-Rad, Davood; Maryn, Youri; Ghaemi, Hamide

doi:10.1016/j.jcomdis.2022.106279

Cited by 6 publications

(1 citation statement)

References 59 publications

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“…Intraclass correlation coefficient values using a two-way mixed effects model, single-measures type, and absolute agreement definition demonstrated high test-retest reliability for the AVQI in Turkish. Furthermore, the considerable values of concurrent validity and diagnostic accuracy of both versions of AVQI-Persian were verified, demonstrating that it can distinguish between normal and diseased voices in Persian speakers [63]. As a conclusion, it is clear that AVQI and similar metrics can be utilized for language-independent screening or diagnosis.…”

Section: Language Factors In Voice Quality Analysismentioning

confidence: 73%

Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones

et al. 2023

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Multiparametric indices offer a more comprehensive approach to voice quality assessment by taking into account multiple acoustic parameters. Artificial intelligence technology can be utilized in healthcare to evaluate data and optimize decision-making processes. Mobile devices provide new opportunities for remote speech monitoring, allowing the use of basic mobile devices as screening tools for the early identification and treatment of voice disorders. However, it is necessary to demonstrate equivalence between mobile device signals and gold standard microphone preamplifiers. Despite the increased use and availability of technology, there is still a lack of understanding of the impact of physiological, speech/language, and cultural factors on voice assessment. Challenges to research include accounting for organic speech-related covariables, such as differences in conversing voice sound pressure level (SPL) and fundamental frequency (f0), recognizing the link between sensory and experimental acoustic outcomes, and obtaining a large dataset to understand regular variation between and within voice-disordered individuals. Our study investigated the use of cellphones to estimate the Acoustic Voice Quality Index (AVQI) in a typical clinical setting using a Pareto-optimized approach in the signal processing path. We found that there was a strong correlation between AVQI results obtained from different smartphones and a studio microphone, with no significant differences in mean AVQI scores between different smartphones. The diagnostic accuracy of different smartphones was comparable to that of a professional microphone, with optimal AVQI cut-off values that can effectively distinguish between normal and pathological voice for each smartphone used in the study. All devices met the proposed 0.8 AUC threshold and demonstrated an acceptable Youden index value.

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Section: Language Factors In Voice Quality Analysismentioning

confidence: 73%