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

Balázs Fodor, Timo Gerkmann
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引用次数: 3

Abstract

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.
基于固定先验和重尾语音模型的语音存在概率估计
语音增强方法通常通过语音存在概率(SPP)估计来增强。然而,SPP估计器受到后验信噪比(SNR)随机波动的影响。虽然有一些建议通过基于平滑观测的SPP框架来克服随机波动,但这些方法没有考虑语音信号的超高斯性质。因此,在本文中,我们定义了一个框架,该框架允许对平滑观察的语音存在的可能性进行建模,同时假设超高斯语音系数。结果表明,该方法在噪声泄漏量和音乐噪声量方面优于参考方法。
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