Objectives Currently there are no objective measures capable of distinguishing between all four voice signal types proposed by Titze in 1995 and updated by Sprecher in 2010. We propose an objective metric that distinguishes between voice signal types based on the aperiodicity present in a signal. Study Design 150 voice signal samples were randomly selected from the Disordered Voice Database and subjectively sorted into the appropriate voice signal category based on the classification scheme presented in Sprecher 2010. Methods Short-time Fourier Transform was applied to each voice sample to produce a spectrum for each signal. The spectrum of each signal was divided into 250 time segments. Next, these segments were compared to each other and used to calculate an outcome named Spectrum Convergence Ratio. Lastly, the mean Spectrum Convergence Ratio was calculated for each of the four voice signal types. Results Spectrum Convergence Ratio was capable of significantly differentiating between each of the four voice signal types (p<0.001). Additionally, this new parameter proved equally as effective at distinguishing between voice signal types as currently available parameters. Conclusion Spectrum Convergence Ratio was capable of objectively distinguishing between all 4 voice signal types. This metric could be used by clinicians to quickly and efficiently diagnose voice disorders and monitor improvements in voice acoustical signals during treatment methods.

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.

Turbulence in the vocal tract creates high-frequency breathiness, causing noise in the acoustical signal of type 4 voice, proving that the acoustical signal does not represent the motion mechanism behind type 4 voice. The results of this study demonstrate that high-speed imaging can provide a more accurate representation of the type 4 vocal fold vibratory pattern, and a more effective method to explore the mechanism of type 4 signals.

Summary Objectives This study aims to build an excised anterior glottic web (AGW) model and study the basic voice-related mechanisms of the AGW through investigating the acoustic, aerodynamic, and vibratory properties. Study Design and Methods Overall, four conditions were tested for each of the eight canine larynges used. At baseline, 10%, 20%, and 33% occlusion (as determined by the placement of the suture), acoustic, aerodynamic, and high-speed video data were collected while each larynx was phonated in a soundproof booth. Results The phonation threshold pressure (PTP) and the phonation threshold flow significantly increased as percent occlusion increased (P < 0.001). There were significant increases in jitter % and shimmer % from baseline group to AGW model groups at PTP, 1.25 PTP, and 1.5 PTP (P = 0.039, P < 0.001, P < 0.001, P < 0.001, P < 0.001, and P = 0.001, respectively). The fundamental frequency significantly increased as percent occlusion increased at all given pressures (P < 0.001). Correlation dimension (D2) was significantly higher in the AGW model groups than in the baseline group at PTP, 1.25 PTP, and 1.5 PTP (P = 0.002, P < 0.001, P = 0.01, respectively). In high-speed videos, the left phase shift in the AGW model groups compared with the baseline at 1.25 PTP was significant (P = 0.027) and right phase shift at 1.5 PTP (P < 0.001). Conclusions We presented an anatomically similar model of a type 1 AGW and confirmed its validity through aerodynamic, acoustic, and high-speed video analysis in our study. We observed and investigated the glottic web movement, which may be a new explanation for the pathologic voice-related mechanism of AGW.

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