Advances in Speech Separation: Techniques, Challenges, and Future Trends
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.
689e9b67a4caabb4320e5d1d
Kai Li
claimed_verified
2025-08-20T08:53:18.572Z
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Kai Li
user
JusperLee
689e9b67a4caabb4320e5d1e
Guo Chen
689e9b67a4caabb4320e5d1f
Wendi Sang
689e9b67a4caabb4320e5d20
Yi Luo
689e9b67a4caabb4320e5d21
Zhuo Chen
689e9b67a4caabb4320e5d22
Shuai Wang
689e9b67a4caabb4320e5d23
Shulin He
689e9b67a4caabb4320e5d24
Zhong-Qiu Wang
689e9b67a4caabb4320e5d25
Andong Li
689e9b67a4caabb4320e5d26
Zhiyong Wu
689e9b67a4caabb4320e5d27
Xiaolin Hu
Advances in Speech Separation: Techniques, Challenges, and Future Trends
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.
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.
DNNs speech separation cocktail party problem learning paradigms separation scenarios supervised frameworks self-supervised frameworks unsupervised frameworks encoders estimation strategies domain-robust frameworks efficient architectures multimodal integration novel self-supervised paradigms