SIPS decomposes stochastic interpolant dynamics into predictive drift and generative denoising to combine arbitrary pretrained predictors with a degradation-agnostic clean-speech prior for better speech enhancement and separation.
Advances in speech separation: Techniques, challenges, and future trends
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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.
years
2026 2representative citing papers
A flow-matching speech separator with biometric best-of-N candidate selection and chunk-wise channel alignment achieves competitive separation metrics and the best downstream ASR/SV error rates among evaluated systems on Libri2Mix.
citing papers explorer
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Predictive-Generative Drift Decomposition for Speech Enhancement and Separation
SIPS decomposes stochastic interpolant dynamics into predictive drift and generative denoising to combine arbitrary pretrained predictors with a degradation-agnostic clean-speech prior for better speech enhancement and separation.
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Flow Matching-Based Speech Source Separation with Best-of-N Biometric Sampling
A flow-matching speech separator with biometric best-of-N candidate selection and chunk-wise channel alignment achieves competitive separation metrics and the best downstream ASR/SV error rates among evaluated systems on Libri2Mix.