Advances in Speech Separation: Techniques, Challenges, and Future Trends
Authors: name: Kai Li, name: Guo Chen, name: Wendi Sang, name: Yi Luo, name: Zhuo Chen, name: Shuai Wang, name: Shulin He, name: Zhong-Qiu Wang, name: Andong Li, name: Zhiyong Wu, name: Xiaolin Hu

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.