Complex Cepstrum-based Decomposition of Speech for Glottal Source Estimation

TL;DR

This paper introduces a fast complex cepstrum-based method for estimating glottal flow from speech signals by separating causal and anticausal components, improving upon existing ZZT decomposition techniques.

Contribution

It proposes a novel, efficient approach using complex cepstrum for glottal flow estimation based on speech signal phase characteristics.

Findings

Effective separation of causal and anticausal components in speech signals.

Higher computational speed compared to ZZT decomposition.

Validation of the method's effectiveness for glottal flow estimation.

Abstract

Homomorphic analysis is a well-known method for the separation of non-linearly combined signals. More particularly, the use of complex cepstrum for source-tract deconvolution has been discussed in various articles. However there exists no study which proposes a glottal flow estimation methodology based on cepstrum and reports effective results. In this paper, we show that complex cepstrum can be effectively used for glottal flow estimation by separating the causal and anticausal components of a windowed speech signal as done by the Zeros of the Z-Transform (ZZT) decomposition. Based on exactly the same principles presented for ZZT decomposition, windowing should be applied such that the windowed speech signals exhibit mixed-phase characteristics which conform the speech production model that the anticausal component is mainly due to the glottal flow open phase. The advantage of the complex cepstrum-based approach compared to the ZZT decomposition is its much higher speed.