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Summary

This study enhances vocal tract modeling for speech analysis by expanding sensitivity functions to nasal sounds. The Gauss-Newton method proves faster for iterative tuning, considering poles and zeros for accurate vocal tract shape determination.

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Area of Science:

  • Acoustics and Speech Science
  • Signal Processing
  • Computational Linguistics

Background:

  • Determining vocal tract cross-sectional areas from speech signals is crucial for speech production research.
  • Iterative methods using sensitivity functions are established for tuning vocal tract models to formant frequencies.

Purpose of the Study:

  • To extend the application of sensitivity functions to a three-tube model for nasal sounds.
  • To compare energy-based and Jacobian-based sensitivity functions for branched-tube vocal tract models.
  • To evaluate iterative tuning methods for a three-tube nasal vocal tract model.

Main Methods:

  • Expansion of sensitivity function concepts to a three-tube model for nasal speech analysis.
  • Comparison of energy-based and Jacobian-based sensitivity functions with respect to formant frequency.
  • Iterative tuning of a three-tube vocal tract model for the nasal sound /m/.
  • Comparative analysis of steepest descent and Gauss-Newton methods for iterative vocal tract tuning.

Main Results:

  • Energy-based and Jacobian-based sensitivity functions show negligible differences when sensitivity is calculated for formant frequency alone.
  • Accurate tuning of the three-tube vocal tract model requires consideration of both polar angle and the absolute value of poles and zeros.
  • The Gauss-Newton method demonstrates faster convergence than the steepest descent method, particularly with a suitable initial guess.

Conclusions:

  • Sensitivity functions can be effectively applied to more complex vocal tract models, including those for nasal sounds.
  • The iterative tuning process for vocal tract models benefits from considering both spectral properties (poles and zeros) and iterative solver efficiency.
  • The Gauss-Newton method is a more efficient iterative solver for vocal tract model tuning compared to the steepest descent method.