Purpose: The overall aim of this study was to apply local intrinsic dimension ( Di) estimation to quantify high-dimensional, disordered voice and discriminate between the 4 types of voice signals. It was predicted that continuous Di analysis throughout the entire time-series would generate comprehensive descriptions of voice signal components, called voice type component profiles (VTCP), that effectively distinguish between the 4 voice types.
Method: One hundred thirty-five voice recording samples of the sustained vowel /a/ were obtained from the Disordered Voice Database Model 4337 and spectrographically classified into the voice type paradigm. The Di and correlation dimension ( D2) were then used to objectively analyze the voice samples and compared based on voice type differentiation efficacy.
Results: The D2 exhibited limited effectiveness in distinguishing between the 4 voice type signals. For Di analysis, significant differences were primarily observed when comparing voice type component 1 (VTC1) and 4 (VTC4) across the 4 voice type signals ( P < .001). The 4 voice type components (VTCs) significantly differentiated between low-dimensional, type 3 and high-dimensional, type 4 signals ( P < .001).
Conclusions: The Di demonstrated improvements over D2 in 2 distinct manners: enhanced resolution at high data dimensions and comprehensive description of voice signal elements.
Keywords: acoustic analysis; intrinsic dimension; nonlinear dynamics; voice; voice disorders; voice type component profile.