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Statistical modeling of speech signals based on generalized gamma distribution
Cited 74 time in
Web of Science
Cited 88 time in Scopus
- Authors
- Issue Date
- 2005-03
- Citation
- IEEE Signal Processing Letters, Vol.12 No.3, pp.258-261
- Abstract
- In this letter, we propose a new statistical model, two-sided generalized gamma distribution (GΓD) for an efficient parametric characterization of speech spectra. GΓD forms a generalized class of parametric distributions, including the Gaussian, Laplacian, and Gamma probability density functions (pdfs) as special cases. We also propose a computationally inexpensive online maximum likelihood (ML) parameter estimation algorithm for GΓD. Likelihoods, coefficients of variation (CVs), and Kolmogorov-Smirnov (KS) tests show that GΓD can model the distribution of the real speech signal more accurately than the conventional Gaussian, Laplacian, Gamma, or generalized Gaussian distribution (GGD). © 2005 IEEE.
- ISSN
- 1070-9908
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