Detecting synchronizations in an asymmetric vocal fold model from time series data

Area of Science:

• Biophysics
• Acoustic phonetics
• Nonlinear dynamics

Background:

• Human voice production involves complex oscillatory dynamics of asymmetric vocal folds.
• Pathological voice conditions can arise from desynchronized vocal fold oscillations.
• Understanding vocal fold synchronization is crucial for diagnosing voice disorders.

Purpose of the Study:

• To develop a nonlinear modeling approach for reconstructing vocal fold synchronization structure.
• To analyze the relationship between asymmetry, subglottal pressure, and synchronization regimes.
• To assess the potential of this approach as a diagnostic tool for voice pathologies.

Main Methods:

• Nonlinear modeling of an asymmetric two-mass model using time series data.
• Reconstruction of the synchronization diagram to identify regimes of synchronization and desynchronization.
• Analysis of chaotic time series data from desynchronized regimes.

Main Results:

• The nonlinear modeling approach successfully reconstructs the synchronization structure.
• Synchronization regimes are dependent on the asymmetry parameter and subglottal pressure.
• The technique effectively predicts pathological phonation associated with vocal fold desynchronization, particularly with chaotic data.

Conclusions:

• The presented nonlinear modeling approach is effective for analyzing vocal fold dynamics.
• It allows for the prediction of pathological phonation regimes from time series data.
• This method shows promise as a diagnostic tool for voice pathologies.