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Time normalization of voice signals using functional data analysis.

J C Lucero1, L L Koenig

  • 1Department of Mathematics, University of Brasilia, Brazil. lucero@mat.unb.br

The Journal of the Acoustical Society of America
|October 29, 2000
PubMed
Summary

Functional data analysis optimizes nonlinear time normalization for accurate harmonics-to-noise ratio (HNR) calculation in voice signals. This advanced method improves voice analysis by preserving signal features and enabling flexible optimization for wavelet alignment.

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Area of Science:

  • Bioacoustics
  • Speech Science
  • Functional Data Analysis

Background:

  • The harmonics-to-noise ratio (HNR) quantifies voice signal irregularity, assuming harmonic and noise components.
  • Accurate HNR computation requires nonlinear time normalization of cycle wavelets to align phase differences.

Purpose of the Study:

  • To apply functional data analysis for optimal nonlinear time normalization and HNR computation in voice signals.
  • To compare functional data analysis with zero-padding, linear normalization, and dynamic programming methods.

Main Methods:

  • Application of functional data analysis for nonlinear time normalization of voice signal cycle wavelets.
  • Computation of HNR using the normalized wavelets.
  • Comparison with results from zero-padding, linear normalization, and dynamic programming algorithms.

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Main Results:

  • Functional data analysis provides an optimal nonlinear normalization for HNR calculation.
  • This method preserves signal shape features and offers differentiable results.
  • The technique is adaptable for simultaneous voice signal analysis (e.g., acoustic, EGG, airflow).

Conclusions:

  • Functional data analysis is a powerful tool for accurate HNR computation and voice signal analysis.
  • It offers advantages in preserving signal integrity and flexibility in optimization criteria.
  • The approach has potential for studying various aspects of the voice production process.