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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.