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Statistical modeling of speech signals based on generalized gamma distribution
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shin, Jong Won | - |
dc.contributor.author | Chang, Joon-Hyuk | - |
dc.contributor.author | Kim, Nam Soo | - |
dc.date.accessioned | 2024-05-13T00:01:06Z | - |
dc.date.available | 2024-05-13T00:01:06Z | - |
dc.date.created | 2023-11-21 | - |
dc.date.created | 2023-11-21 | - |
dc.date.issued | 2005-03 | - |
dc.identifier.citation | IEEE Signal Processing Letters, Vol.12 No.3, pp.258-261 | - |
dc.identifier.issn | 1070-9908 | - |
dc.identifier.uri | https://hdl.handle.net/10371/201490 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.title | Statistical modeling of speech signals based on generalized gamma distribution | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/LSP.2004.840869 | - |
dc.citation.journaltitle | IEEE Signal Processing Letters | - |
dc.identifier.wosid | 000227130500023 | - |
dc.identifier.scopusid | 2-s2.0-14644439205 | - |
dc.citation.endpage | 261 | - |
dc.citation.number | 3 | - |
dc.citation.startpage | 258 | - |
dc.citation.volume | 12 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Kim, Nam Soo | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
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