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.
Results from these classifications highlight the differences between swallowing function in patients with early and mid-stage PD and healthy controls. Early identification of swallowing dysfunction is key to developing preventative swallowing treatments for those with PD.
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.
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