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

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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.
Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
PublisherInternational Speech Communication Association
Number of pages5
ISBN (Print)978-1-5108-4876-4
Publication statusPublished - Aug 2017
MoE publication typeA4 Article in a conference publication
EventInterspeech - Stockholm, Sweden
Duration: 20 Aug 201724 Aug 2017
Conference number: 18

Publication series

NameInterspeech: Annual Conference of the International Speech Communication Association
ISSN (Electronic)1990-9772


Internet address


  • probability distribution mixture models
  • speech production modeling


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