A speech presence probability estimator based on fixed priors and a heavy-tailed speech model

Speech enhancement approaches are often enhanced by speech presence probability (SPP) estimation. However, SPP estimators suffer from random fluctuations of the a posteriori signal-to-noise ratio (SNR). While there exist proposals that overcome the random fluctuations by basing the SPP framework on...

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Bibliographic Details
Main Authors: Fodor, Balazs, Gerkmann, Timo
Format: Conference Proceeding
Language:English
Subjects:
Estimation
Shape
Signal to noise ratio
Speech
Speech enhancement
Online Access:Request full text
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Summary:Speech enhancement approaches are often enhanced by speech presence probability (SPP) estimation. However, SPP estimators suffer from random fluctuations of the a posteriori signal-to-noise ratio (SNR). While there exist proposals that overcome the random fluctuations by basing the SPP framework on smoothed observations, these approaches do not take into account the super-Gaussian nature of speech signals. Thus, in this paper we define a framework that allows for modeling the likelihoods of speech presence for smoothed observations, while at the same time assuming super-Gaussian speech coefficients. The proposed approach is shown to outperform the reference approaches in terms of the amount of noise leakage and the amount of musical noise.
ISSN:2219-5491
2219-5491