The progressive paradigm is a promising strategy to optimize network performance for speech enhancement purposes. Recent works have shown different strategies to improve the accuracy of speech enhancement solutions based on this mechanism. This paper studies the progressive speech enhancement using convolutional and residual neural network architectures and explores two criteria for loss function optimization: weighted and uniform progressive. This work carries out the evaluation on simulated and real speech samples with reverberation and added noise using REVERB and VoiceHome datasets. Experimental results show a variety of achievements among the loss function optimization criteria and the network architectures. Results show that the progressive design strengthens the model and increases the robustness to distortions due to reverberation and noise.

中文翻译：

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使用深度神经网络进行语音增强的渐进损失函数

渐进范式是一种有前景的策略，可以优化网络性能以实现语音增强目的。最近的工作已经展示了不同的策略来提高基于这种机制的语音增强解决方案的准确性。本文研究了使用卷积和残差神经网络架构的渐进式语音增强，并探讨了损失函数优化的两个标准：加权和均匀渐进。这项工作使用 REVERB 和 VoiceHome 数据集对带有混响和添加噪声的模拟和真实语音样本进行评估。实验结果显示了损失函数优化标准和网络架构之间的各种成就。