Causal-anticausal Decomposition Of Speech Using Complex Cepstrum For Glottal Source Estimation

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.

Stats

• citations44
• S2 citations—
• github stars0
• HF likes0
• heat score12.40
• arxiv keydrugman2019causal

Related papers

• Complex Cepstrum-based Decomposition Of Speech For Glottal Source Estimation (2019)10.74
• Chirp Complex Cepstrum-based Decomposition For Asynchronous Glottal Analysis (2020)0.00
• Combination Of Linear Prediction And Phase Decomposition For Glottal Source Analysis On Voiced Speech (2016)0.00
• Glottal Source Estimation Robustness: A Comparison Of Sensitivity Of Voice Source Estimation Techniques (2020)0.00
• A Comparative Study Of Glottal Source Estimation Techniques (2019)14.35
• Phase-incorporating Speech Enhancement Based On Complex-valued Gaussian Process Latent Variable Model (2016)0.00
• Complex Frequency Domain Linear Prediction: A Tool To Compute Modulation Spectrum Of Speech (2022)3.58
• Complex Recurrent Variational Autoencoder With Application To Speech Enhancement (2022)0.00