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Testing the assumptions of linear prediction analysis in normal vowels
M A Little1, P E McSharry, I M Moroz
1Applied Dynamical Systems Research Group, Oxford Centre for Industrial and Applied Mathematics, and Pattern Analysis Research Group, Engineering Science, Oxford University, United Kingdom. littlem@maths.ox.ac.uk
The Journal of the Acoustical Society of America
|February 4, 2006
Summary
Gaussian linear prediction analysis fails to capture all speech dynamics. Improved surrogate data testing reveals nonlinearities and non-Gaussianity are crucial for accurate speech modeling.
Area of Science:
- Speech processing
- Dynamical systems theory
- Acoustic phonetics
Background:
- Gaussian linear prediction analysis is widely used in speech technology.
- Current speech analysis often assumes linear and Gaussian dynamics.
- Recent research explores nonlinear and non-Gaussian models for speech.
Purpose of the Study:
- To test the validity of Gaussian linear prediction analysis for speech dynamics.
- To provide experimental evidence for nonlinearity and/or non-Gaussianity in speech signals.
- To investigate the success of linear Gaussian models as approximations.
Main Methods:
- Development of an improved surrogate data testing method.
- Application of the test to U.S. English vowels from male and female speakers.
- Circumvention of known surrogate data testing problems through calibrated statistics and experimental protocols.
Main Results:
- Experimental evidence shows Gaussian linear prediction analysis cannot extract all dynamical structure from speech time series.
- Robust evidence undermines the validity of purely linear techniques.
- Findings support the presence of dynamical nonlinearity and/or non-Gaussianity in speech.
Conclusions:
- Linear Gaussian models are insufficient for fully capturing speech dynamics.
- Nonlinear and non-Gaussian dynamics are likely present in real speech.
- Hybrid linear/nonlinear/non-Gaussian models are recommended for accurate speech modeling.