NDLI: Accurate estimation of the glottal flow derivative using iteratively reweighted 1-norm minimization

Content Provider	IEEE Xplore Digital Library
Author	Lankarrany, M. Zhu, W.P. Swamy, M.N.S.
Copyright Year	2011
Description	Author affiliation: Department of Electrical and Computer Engineering, Concordia University Montreal, QC, Canada (Lankarrany, M.; Zhu, W.P.; Swamy, M.N.S.)
Abstract	The problem of estimating the exact shape of the glottal flow derivative (GFD) using reweighted 1-norm minimization of the second derivative of the GFD is addressed in this paper. By using physiological models of the glottal flow derivative, such as the Liljencrants-Fant (LF) and Rosenberg models, it is intuitively found that the second derivative of those models is highly sparse. Based on this observation an iteratively reweighted 1-norm minimization algorithm is proposed to accurately estimate the vocal tract of the speech signal by exploiting the sparsity of the second derivative of the GFD (the residual of the linear prediction model). An experimental study using a data set of 40 vowels /a/ and /e/, 20 for each, is conducted, showing the efficiency, in terms of the number of iterations and the total run-time reduction, of the proposed algorithm. Furthermore, the results of estimating the GFD of two vowels /a/ & /e/ using Joint Source-Filter Model Optimization and our proposed method, demonstrate the accuracy, in terms of similarity to the physiological model and precise synthesis, of our proposed algorithm.
Starting Page	33
Ending Page	36
File Size	230202
Page Count	4
File Format	PDF
ISBN	9781612841359
e-ISBN	9781612841373
DOI	10.1109/NEWCAS.2011.5981212
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-26
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech Minimization Speech processing Shape Physiology Speech coding Prediction algorithms
Content Type	Text
Resource Type	Article

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