語音分離技術的進展：方法、挑戰與未來趨勢

摘要

語音分離領域，針對「雞尾酒會問題」，已因深度神經網絡（DNN）的應用而迎來革命性進展。語音分離提升了複雜聲學環境下的清晰度，並作為語音識別和說話人識別的重要預處理步驟。然而，當前文獻多聚焦於特定架構或孤立方法，導致理解碎片化。本綜述旨在彌補這一缺口，系統性地審視基於DNN的語音分離技術。我們的工作通過以下幾點與眾不同：（一）全面視角：我們系統探討了學習範式、已知/未知說話者的分離場景、監督/自監督/無監督框架的對比分析，以及從編碼器到估計策略的架構組件。（二）時效性：涵蓋前沿發展，確保讀者能接觸到最新的創新與基準測試。（三）獨到見解：超越總結，我們評估技術發展軌跡，識別新興模式，並強調包括領域魯棒框架、高效架構、多模態整合及新穎自監督範式在內的潛力方向。（四）公正評估：我們在標準數據集上進行定量評估，揭示不同方法的真實能力與局限。這份全面綜述為經驗豐富的研究者及初入此複雜領域的新手提供了易於理解的參考指南。

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.

語音分離技術的進展：方法、挑戰與未來趨勢

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

摘要

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