Causal-Anticausal Decomposition of Speech using Complex Cepstrum for Glottal Source Estimation

TL;DR

This paper explores using complex cepstrum for efficient glottal flow estimation, demonstrating comparable accuracy to ZZT while offering faster computation, with potential applications in voice quality analysis.

Contribution

It introduces a novel application of complex cepstrum for glottal source estimation, showing its effectiveness and speed advantages over existing ZZT-based methods.

Findings

Complex cepstrum effectively decomposes speech into causal and anticausal components.

The method achieves similar glottal estimates as ZZT but with higher computational speed.

Potential for use in voice quality analysis on large speech datasets.

Abstract

Complex cepstrum is known in the literature for linearly separating causal and anticausal components. Relying on advances achieved by the Zeros of the Z-Transform (ZZT) technique, we here investigate the possibility of using complex cepstrum for glottal flow estimation on a large-scale database. Via a systematic study of the windowing effects on the deconvolution quality, we show that the complex cepstrum causal-anticausal decomposition can be effectively used for glottal flow estimation when specific windowing criteria are met. It is also shown that this complex cepstral decomposition gives similar glottal estimates as obtained with the ZZT method. However, as complex cepstrum uses FFT operations instead of requiring the factoring of high-degree polynomials, the method benefits from a much higher speed. Finally in our tests on a large corpus of real expressive speech, we show that the proposed method has the potential to be used for voice quality analysis.