Advances in Speech Separation: Techniques, Challenges, and Future Trends
JusperLee
Abstract
A survey of DNN-based speech separation techniques, covering learning paradigms, separation scenarios, and architectural components, with a focus on current advancements and promising future directions.
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.
Community
We've just published "Advances in Speech Separation: Techniques, Challenges, and Future Trends" - a systematic review that addresses the fragmented landscape in this rapidly evolving field.
🔍 What we accomplished: • Comprehensive coverage : Systematically reviewed ALL deep learning-based speech separation techniques from 2016-2025
• Complete learning paradigms : From supervised to self-supervised and unsupervised frameworks
• Fair benchmarking : Rigorous quantitative evaluations across standard datasets (WSJ0-2Mix, WHAM!, LibriMix) with unified experimental framework
• Cutting-edge insights : Latest technological roadmap including emerging approaches like LLM-based solutions, diffusion models, and multimodal integration
📊 Key contributions:
- 69+ models analyzed with performance comparisons
- Identification of promising research directions
- Critical evaluation of technological trajectories
- Open-source toolkit summaries (Asteroid, SpeechBrain, WeSep)
🙏 Special thanks to @Guo Chen and @Wendi Sang for their invaluable help in organizing methodologies, and all co-authors for their crucial guidance throughout this project.
🌐 Resources for the community:
📄 Paper : https://arxiv.org/pdf/2508.10830
🔗 Interactive Website : https://cslikai.cn/Speech-Separation-Paper-Tutorial
💻 GitHub Repository : https://github.com/JusperLee/Speech-Separation-Paper-Tutorial
This work aims to serve as both an accessible reference for newcomers and a comprehensive guide for experienced researchers navigating the complex landscape of speech separation.
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