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首页> 外文期刊>IEEE Transactions on Speech and Audio Proceessing >Speech Enhancement Based on Perceptually Motivated Bayesian Estimators of the Magnitude Spectrum
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Speech Enhancement Based on Perceptually Motivated Bayesian Estimators of the Magnitude Spectrum

机译：基于幅度谱的感知动机贝叶斯估计的语音增强

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摘要

The traditional minimum mean-square error (MMSE) estimator of the short-time spectral amplitude is based on the minimization of the Bayesian squared-error cost function. The squared-error cost function, however, is not subjectively meaningful in that it does not necessarily produce estimators that emphasize spectral peak (formants) information or estimators which take into account auditory masking effects. To overcome the shortcomings of the MMSE estimator, we propose in this paper Bayesian estimators of the short-time spectral magnitude of speech based on perceptually motivated cost functions. In particular, we use variants of speech distortion measures, such as the Itakura–Saito and weighted likelihood-ratio distortion measures, which have been used successfully in speech recognition. Three classes of Bayesian estimators of the speech magnitude spectrum are derived. The first class of estimators emphasizes spectral peak information, the second class uses a weighted-Euclidean cost function that implicitly takes into account auditory masking effects, and the third class of estimators is designed to penalize spectral attenuation. Of the three classes of Bayesian estimators, the estimators that implicitly take into account auditory masking effect performed the best in terms of having less residual noise and better speech quality.
机译：短时频谱幅度的传统最小均方误差（MMSE）估计器基于贝叶斯平方误差成本函数的最小化。但是，平方误差成本函数在主观上并不有意义，因为它不一定会产生强调频谱峰值（共振峰）信息的估计量或考虑听觉掩盖效应的估计量。为了克服MMSE估计器的缺点，我们在本文中提出了基于感知动机成本函数的贝叶斯估计器，用于语音的短时频谱幅度。特别是，我们使用了语音失真度量的变体，例如Itakura–Saito和加权似然比失真度量，这些度量已成功用于语音识别中。得出语音幅度谱的三类贝叶斯估计量。第一类评估器强调频谱峰值信息，第二类评估器使用加权欧几里德成本函数，该函数隐式考虑了听觉掩蔽效应，第三类评估器旨在惩罚频谱衰减。在三类贝叶斯估计器中，隐含考虑听觉掩盖效应的估计器在减少残留噪声和改善语音质量方面表现最佳。

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