Deep learning technology has been widely applied to speech enhancement. While testing the effectiveness of various network structures, researchers are also exploring the improvement of the loss function used in network training. Although the existing methods have considered the auditory characteristics of speech or the reasonable expression of signal-to-noise ratio, the correlation with the auditory evaluation score and the applicability of the calculation for gradient optimization still need to be improved. In this paper, a signal-to-noise ratio loss function based on auditory power compression is proposed. The experimental results show that the overall correlation between the proposed function and the indexes of objective speech intelligibility, which is better than other loss functions. For the same speech enhancement model, the training effect of this method is also better than other comparison methods.

翻译：深度学习技术已广泛应用于语音增强领域。在测试各种网络结构有效性的同时，研究者也在探索网络训练中所用损失函数的改进。尽管现有方法已考虑了语音的听觉特性或信噪比的合理表达，但其与听觉评估分数的相关性及梯度优化的计算适用性仍有待提升。本文提出一种基于听觉功率压缩的信噪比损失函数。实验结果表明，该函数与客观语音可懂度指标的整体相关性优于其他损失函数。对于相同的语音增强模型，该方法的训练效果也优于其他对比方法。