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

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A4 Artikkeli konferenssijulkaisussa

Date

2017-08

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en

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5

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Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, Volume 2017-August, pp. 344-348, Interspeech: Annual Conference of the International Speech Communication Association

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.

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Keywords

probability distribution mixture models, speech production modeling

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Citation

Bäckström, T 2017, Estimation of the Probability Distribution of Spectral Fine Structure in the Speech Source . in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH . vol. 2017-August, Interspeech: Annual Conference of the International Speech Communication Association, International Speech Communication Association (ISCA), pp. 344-348, Interspeech, Stockholm, Sweden, 20/08/2017 . https://doi.org/10.21437/Interspeech.2017-389