Using Rate of Divergence as an Objective Measure to Differentiate between Voice Signal Types Based on the Amount of Disorder in the Signal

Abstract

Objective/Hypothesis The purpose of this paper is to introduce rate of divergence as an objective measure to differentiate between the four voice types based on the amount of disorder present in a signal. We hypothesized that rate of divergence would provide an objective measure that can quantify all four voice types. Study Design 150 acoustic voice recordings were randomly selected and analyzed using traditional perturbation, nonlinear, and rate of divergence analysis methods. ty Methods We developed a new parameter, rate of divergence, which uses a modified version of Wolf’s algorithm for calculating Lyapunov exponents of a system. The outcome of this calculation is not a Lyapunov exponent, but rather a description of the divergence of two nearby data points for the next three points in the time series, followed in three time delayed embedding dimensions. This measure was compared to currently existing perturbation and nonlinear dynamic methods of distinguishing between voice signals. Results There was a direct relationship between voice type and rate of divergence. This calculation is especially effective at differentiating between type 3 and type 4 voices (p<0.001), and is equally effective at differentiating type 1, type 2, and type 3 signals as currently existing methods. Conclusion The rate of divergence calculation introduced is an objective measure that can be used to distinguish between all four voice types based on amount of disorder present, leading to quicker and more accurate voice typing as well as an improved understanding of the nonlinear dynamics involved in phonation.

Comparing the Nonlinear Dynamic Acoustic Parameters of Healthy Adult and Pediatric Voices

“…When comparing all subjects in both groups, χ 2 analysis showed that the reference SCR and ROD values established using the adult population could correctly classify voices as ordered or disordered with higher accuracy in adults compared with pediatric subjects. The higher proportion of correctly typed adult voices confirms that the adult normative reference values for SCR and ROD established by Calawerts et al 8 and Lin et al 9 are sufficient for classifying adult voices but not pediatric voices. In general, both SCR and ROD classified pediatric voices as 1 voice type higher than the voice type confirmed by spectral analysis.…”

“…9 Briefly, an SCR of 0 indicates high voice aperiodicity, and a value of 1 indicates more periodicity. A detailed description of ROD derivation and calculation can be found in Calawerts et al 8 Briefly, an ROD value of 0 indicates high periodicity in a voice sample, whereas a value of 1 indicates severe aperiodicity.…”

“…23,24 ROD measures the nonlinear divergence of an acoustic waveform from its previous pattern, or its unpredictability, on the basis of the fundamentals of the Lyapunov exponent. 8 A higher ROD represents more voice disorder. Of note, ROD distinguishes among each of the 4 voice types and was especially useful for discriminating between voice types 3 and 4.…”

Application of Local Intrinsic Dimension for Acoustical Analysis of Voice Signal Components

“…12,24,25 25 Specifically, high degrees of noise can obscure the self-similarity property of voice signals, leading to increased computational errors and invalid calculations. 23,[41][42][43] Consequently, these findings suggest that D 2 analysis should be cautiously applied to voice signal types 1, 2, and 3 but not 4.…”

Invariant Measures Based on the U‐Correlation Integral: An Application to the Study of Human Voice

“…They reported an estimation of 2 for normal (mean value 2 = 0.014) and esophageal phonation (mean value 2 = 0.023) subjects. In [27], Calawerts et al calculated the largest Lyapunov exponent for type 1, type 2, and type 3 voices using the Kay Elemetrics database. Their results suggest an increase of the value of the largest Lyapunov exponent along with the type of the voice.…”