Advances in Speech Separation: Techniques, Challenges, and Future Trends
Kai Li, Guo Chen, Wendi Sang, Yi Luo, Zhuo Chen, Shuai Wang, Shulin He, Zhong-Qiu Wang, Andong Li, Zhiyong Wu, Xiaolin Hu
2025-08-20
Summary
This paper is a review of how deep learning has been used to separate speech, like picking out one voice in a noisy room, and it covers different ways these systems are built and tested, looking at what's new and what might come next.
What's the problem?
Even though deep learning is great for speech separation, the research is spread out, with papers focusing on just one type of system or one specific problem, making it hard to get a full picture of the whole field and how different methods compare.
What's the solution?
This paper tries to fix that by giving a complete overview of deep learning methods for speech separation. It examines different learning styles, situations with known or unknown speakers, and compares various ways to build these systems, from how they process sound to how they guess the separate voices, also covering the latest advancements and testing them fairly.
Why it matters?
Understanding speech separation is important because it makes conversations clearer in noisy places and helps computers understand us better for things like voice assistants. This paper makes it easier for both new and experienced researchers to learn about and improve these helpful technologies.
Abstract
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.