随着深度学习技术的兴起，基于深度学习的语音增强方法日益广泛应用，性能普遍优于传统方法。概述语音增强中降噪信号处理的基本框架，逐步分析深度学习驱动的语音增强模型的最新进展。对基于深度学习的语音增强算法进行全面整理，详细介绍不同神经网络的语音增强方法的原理、特点、评价指标及代表性研究，综合评估这些方法的优势与不足。最后，结合当前发展状况，分析语音增强过程中面临的核心挑战，并对未来发展路径进行讨论与预测。

With the emergence of deep learning technologies, speech enhancement methods based on deep learning have seen widespread application and generally surpass traditional approaches in performance. The fundamental framework of noise reduction signal processing in speech enhancement was outlined and progressively delved into the latest advancements in deep learning-driven speech enhancement models. A comprehensive organization of deep learning-based speech enhancement algorithms was provided, detailing the principles, characteristics, evaluation metrics, and representative studies of various neural network-based methods. The advantages and limitations of these approaches were thoroughly assessed. Finally, in light of the current developmental landscape, the core challenges encountered in the speech enhancement process were analyzed, and future developmental trajectories were discussed and predicted.