Estimation of the Probability Distribution of Spectral Fine Structure in the Speech Source

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Bäckström, Tom
dc.date.accessioned 2017-11-21T13:37:56Z
dc.date.available 2017-11-21T13:37:56Z
dc.date.issued 2017-08
dc.identifier.citation Bäckström , T 2017 , Estimation of the Probability Distribution of Spectral Fine Structure in the Speech Source . in Proceedings of Interspeech 2017 . Interspeech: Annual Conference of the International Speech Communication Association , International Speech Communication Association , pp. 344-348 , Interspeech , Stockholm , Sweden , 20-24 August . DOI: 10.21437/Interspeech.2017-389 en
dc.identifier.issn 1990-9772
dc.identifier.other PURE UUID: 9cc90495-4a9b-4c67-aeff-3e88bb21b061
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/estimation-of-the-probability-distribution-of-spectral-fine-structure-in-the-speech-source(9cc90495-4a9b-4c67-aeff-3e88bb21b061).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/15741726/Backstrom_interspeech_0389.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/28835
dc.description.abstract The efficiency of many speech processing methods rely on accurate modeling of the distribution of the signal spectrum and a majority of prior works suggest that the spectral components follow the Laplace distribution. To improve the probability distribution models based on our knowledge of speech source modeling, we argue that the model should in fact be a multiplicative mixture model, including terms for voiced and unvoiced utterances. While prior works have applied Gaussian mixture models, we demonstrate that a mixture of generalized Gaussian models more accurately follows the observations. The proposed estimation method is based on measuring the ratio of $L_p$-norms between spectral bands. Such ratios follow the Beta-distribution when the input signal is generalized Gaussian, whereby the estimated parameters can be used to determine the underlying parameters of the mixture of generalized Gaussian distributions. en
dc.format.extent 5
dc.format.extent 344-348
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartof Interspeech en
dc.relation.ispartofseries Proceedings of Interspeech 2017 en
dc.relation.ispartofseries Interspeech: Annual Conference of the International Speech Communication Association en
dc.rights openAccess en
dc.subject.other 112 Statistics and probability en
dc.subject.other 113 Computer and information sciences en
dc.title Estimation of the Probability Distribution of Spectral Fine Structure in the Speech Source en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Signal Processing and Acoustics
dc.subject.keyword probability distribution mixture models
dc.subject.keyword speech production modeling
dc.subject.keyword 112 Statistics and probability
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201711217656
dc.identifier.doi 10.21437/Interspeech.2017-389
dc.type.version publishedVersion


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