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