Quasi Closed Phase Glottal Inverse Filtering Analysis With Weighted Linear Prediction

Authors:
Manu Airaksinen;Tuomo Raitio;Brad Story;Paavo Alku
Affiliations:
Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland;Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland;Department of Speech, Language and Hearing Sciences, University of Arizona, USA;Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland
Venue:
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Year:
2014

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Abstract

This study presents a new glottal inverse filtering (GIF) technique based on closed phase analysis over multiple fundamental periods. The proposed quasi closed phase (QCP) analysis method utilizes weighted linear prediction (WLP) with a specific attenuated main excitation (AME) weight function that attenuates the contribution of the glottal source in the linear prediction model optimization. This enables the use of the autocorrelation criterion in linear prediction in contrast to the covariance criterion used in conventional closed phase analysis. The QCP method was compared to previously developed methods by using synthetic vowels produced with the conventional source-filter model as well as with a physical modeling approach. The obtained objective measures show that the QCP method improves the GIF performance in terms of errors in typical glottal source parametrizations for both low- and high-pitched vowels. Additionally, QCP was tested in a physiologically oriented vocoder, where the analysis/synthesis quality was evaluated with a subjective listening test indicating improved perceived quality for normal speaking style.