A new method for voice source estimation is evaluated and compared to Linear Prediction (LP) inverse filtering methods (autocorrelation LPC, covariance LPC and IAIF [1]). The method is based on a causal/anticausal model of the voice source and the ZZT (Zeros of Z-Transform) representation [2] for causal/anticausal signal separation. A database containing synthetic speech with various voice source settings and natural speech with acoustic and electro-glottographic signals was recorded. Formal evaluation of source estimation is based on spectral distances. The results show that the ZZT causal/anticausal decomposition method outperforms LP in voice source estimation both for synthetic and natural signals. However, its computational load is much heavier (despite a very simple principle) and the method seems sensitive to noise and computation precision errors.
Cite as: Sturmel, N., D'Alessandro, C., Doval, B. (2007) A comparative evaluation of the zeros of z transform representation for voice source estimation. Proc. Interspeech 2007, 558-561, doi: 10.21437/Interspeech.2007-249
@inproceedings{sturmel07_interspeech,
author={Nicolas Sturmel and Christophe D'Alessandro and Boris Doval},
title={{A comparative evaluation of the zeros of z transform representation for voice source estimation}},
year=2007,
booktitle={Proc. Interspeech 2007},
pages={558--561},
doi={10.21437/Interspeech.2007-249}
}