Diagnostic Accuracy of Acoustic Voice Quality Index Version 02.03 in Discriminating across the Perceptual Degrees of Dysphonia Severity in Kannada Language

Pebbili, Gopi Kishore; Shabnam, Srushti; Pushpavathi, M; J, Rashmi; Sankar, R. Gopi; Nethra, R.; Shreya, Sivaramakrishnan; Ghimire, Shashish

doi:10.1016/j.jvoice.2019.07.010

Cited by 10 publications

(1 citation statement)

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“…The findings of this study might be used as an objective criterion to detect voice disorders. According to [60], the AVQI is a robust multiparametric measure that can reliably differentiate between subcategories of severity of perceptual dysphonia with good accuracy in Kannada. Moreover, AVQI has been shown to be useful in analyzing signals with greater degrees of aperiodicity, such as extreme hoarse voice quality [7].…”

Section: Language Factors In Voice Quality Analysismentioning

confidence: 99%

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: 99%