Author

Yan-Min Qian

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#artificial intelligence Preprint Oct 2026

SLM-SS: Token-Based Speech Separation with Speech Language Models

SLM-SS is proposed, a token-based generative speech separation framework that models multi-talker mixtures with speech language models and introduces a per-talker non-autoregressive strategy that simplifies high-order modeling and improves the stability.

Chen-Da Li, Tian-Hua Li, Yi-Ming Chen et al. · 0 citations

#artificial intelligence Jun 2026

TF-MoE: Time-Frequency Mixture-of-Experts for Efficient Speech Separation

TF-MoE, a sparse Mixture-of-Experts (MoE) framework that enhances model capacity with almost no increase in inference cost, is proposed and demonstrated that TF-MoE consistently improves separation performance under computation cost constraints.

Qin-Zhe Hu, Chenda Li, Wangyou Zhang et al. · arXiv.org · 3 citations · ⚡1