语音分离技术的进展：方法、挑战与未来趋势

摘要

语音分离领域，致力于解决“鸡尾酒会问题”，随着深度神经网络（DNNs）的应用取得了革命性进展。语音分离不仅提升了复杂声学环境下的语音清晰度，还作为语音识别和说话人识别的关键预处理步骤。然而，现有文献多局限于特定架构或孤立方法，导致理解碎片化。本综述旨在填补这一空白，系统性地审视基于DNN的语音分离技术。我们的工作特色在于：（一）全面视角：系统探讨学习范式、已知/未知说话人场景下的分离、监督/自监督/无监督框架的对比分析，以及从编码器到估计策略的架构组件。（二）时效性：涵盖前沿发展，确保读者触及当前创新与基准。（三）独到见解：超越总结，评估技术轨迹，识别新兴趋势，并强调包括领域鲁棒框架、高效架构、多模态融合及新型自监督范式在内的有前景方向。（四）公正评估：基于标准数据集进行量化评估，揭示不同方法的真实能力与局限。这份全面综述为经验丰富的研究者及初入此复杂领域的新手提供了易于理解的参考指南。

English

The field of speech separation, addressing the "cocktail party problem", has seen revolutionary advances with DNNs. Speech separation enhances clarity in complex acoustic environments and serves as crucial pre-processing for speech recognition and speaker recognition. However, current literature focuses narrowly on specific architectures or isolated approaches, creating fragmented understanding. This survey addresses this gap by providing systematic examination of DNN-based speech separation techniques. Our work differentiates itself through: (I) Comprehensive perspective: We systematically investigate learning paradigms, separation scenarios with known/unknown speakers, comparative analysis of supervised/self-supervised/unsupervised frameworks, and architectural components from encoders to estimation strategies. (II) Timeliness: Coverage of cutting-edge developments ensures access to current innovations and benchmarks. (III) Unique insights: Beyond summarization, we evaluate technological trajectories, identify emerging patterns, and highlight promising directions including domain-robust frameworks, efficient architectures, multimodal integration, and novel self-supervised paradigms. (IV) Fair evaluation: We provide quantitative evaluations on standard datasets, revealing true capabilities and limitations of different methods. This comprehensive survey serves as an accessible reference for experienced researchers and newcomers navigating speech separation's complex landscape.

语音分离技术的进展：方法、挑战与未来趋势

Advances in Speech Separation: Techniques, Challenges, and Future Trends

摘要

Support