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Closed phase covariance analysis based on constrained linear prediction for glottal inverse filtering
Paavo Alku1, Carlo Magi, Santeri Yrttiaho
1Department of Signal Processing and Acoustics, Helsinki University of Technology, P.O. Box 3000, Fi-02015 TKK, Finland. paavo.alku@tkk.fi
The Journal of the Acoustical Society of America
|May 12, 2009
Summary
This study enhances glottal inverse filtering by modifying closed phase (CP) covariance analysis. The improved method uses a constrained dc gain and minimum phase inverse filter for more accurate vocal tract modeling.
Area of Science:
- Speech Science
- Signal Processing
Background:
- Closed phase (CP) covariance analysis is a standard glottal inverse filtering technique.
- Linear prediction (LP) vocal tract estimation during the short CP is sensitive to frame position.
Purpose of the Study:
- To improve the accuracy and robustness of CP covariance analysis for glottal inverse filtering.
- To address limitations in vocal tract modeling due to short CP duration and frame position sensitivity.
Main Methods:
- Modified the conventional LP approach by constraining the dc gain of the inverse filter during optimization.
- Incorporated a minimum phase inverse filter into the CP covariance analysis algorithm.
- Evaluated the modified method using synthetic vowels and natural speech data.
Main Results:
- The constrained dc gain promotes vocal tract models consistent with source-filter theory, favoring realistic formant resonances.
- The new algorithm demonstrated improved performance in glottal inverse filtering.
- Enhanced robustness of the CP covariance analysis concerning the covariance frame position.
Conclusions:
- The modified CP covariance analysis offers a more reliable method for vocal tract estimation in glottal inverse filtering.
- The proposed enhancements lead to more accurate and stable vocal tract models, particularly in challenging speech segments.
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