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: One hundred fifty voice signal samples were randomly selected from the Disordered Voice Database and subjectively sorted into the appropriate voice signal category on the basis of 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 (SCR). Finally, the mean SCR was calculated for each of the four voice signal types.
Results: SCR 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: SCR was capable of objectively distinguishing between all four 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.
Keywords: Short time Fourier transform; Signal spectrum analysis; Spectrum convergence ratio; Turbulence; Voice signal classification.
Copyright © 2016 The Voice Foundation. Published by Elsevier Inc. All rights reserved.