The Effectiveness of Acoustic Voice Quality Index
\nto Identify People With Parkinson’s Disease

Developing portable computerized diagnostic tools for Parkinson’s disease (PD) based on
\nParkinsonian dysarthria requires identifying the voice features most sensitive to PD symptoms. Numerous
\nstudies have explored the correlations of various acoustic features with PD diagnosis and progression.
\nDespite achieving high accuracies in some cases, these proposed features or methods have not yet been
\nintegrated into publicly available PD diagnostic tools due to their limited robustness. The Acoustic Voice
\nQuality Index (AVQI), introduced by Maryn, has the potential to be a voice severity measure capable of
\ndistinguishing people with PD from healthy individuals. This study investigates the effectiveness of AVQI in
\nidentifying people with PD and the reliable preprocessing techniques to compute AVQI from short-duration
\nsustained vowels. AVQI data extracted from the PC-GITA dataset underwent several statistical analyses to
\ncompare the validity of AVQI using truncated and concatenated sustained vowels. Statistical distribution
\nand receiver operating characteristic (ROC) curve analyses were used to assess AVQI’s effectiveness. The
\nresults demonstrate that AVQI can distinguish PD among female participants with an accuracy of 75.33%
\nand an area under the curve (AUC) of 0.74. These findings also confirm the suitability of using concatenated
\nsustained vowels for calculating AVQI from short recordings. They highlight AVQI’s potential application
\nin portable PD diagnostic tools, underscoring its relevance in clinical practice.