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Spectral Phase Estimation Based on Deep Neural Networks for Single Channel Speech Enhancement

机译：基于深神经网络的单频语语音增强的光谱相位估计

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摘要

Majority of speech processing algorithms operate only with the spectral magnitude, leaving spectral phase unstructured and unexplored. With recent advancement in deep neural networks (DNNs), the phase processing became more important as an innovative and emergent prospective of the DNN based speech enhancement. In this paper, a speech enhancement method based on DNN combined with spectral phase estimation is proposed to improve the quality and intelligibility of the noisy speech. During training, DNNs are trained to learn a mapping from the noisy speech utterances and predict the coefficient to construct an ideal ratio mask for the spectral magnitude. The temporal smoothing unwrapped spectral phase estimation is incorporated as a target and transformed into a structured spectral phase during signal reconstruction. In enhancement stage, the enhanced speech magnitude is reconstructed with estimated structured spectral phase. Experimental results demonstrate success of the proposed method for speech enhancement in terms of the speech quality and intelligibility.
机译：大多数语音处理算法仅用频谱幅度运行，留下光谱相位非结构化和未开发的。随着近期神经网络（DNN）的进步，相位处理变得更加重要，作为基于DNN的语音增强的创新和紧急前瞻性。本文提出了一种基于DNN与光谱相位估计相结合的语音增强方法，提高了噪声语音的质量和可懂度。在训练期间，培训DNN以从嘈杂的语音发声中学习映射，并预测系数以构建用于光谱幅度的理想比率掩模。时间平滑未包装的光谱相位估计被用作目标并在信号重建期间转换为结构化光谱相。在增强阶段，利用估计的结构化光谱相重建增强的语音幅度。实验结果表明，在语音质量和可懂度方面提出了语音增强方法的成功。

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