SLM-SS: Token-Based Speech Separation with Speech Language Models
Speech separation (SS) has witnessed substantial progress in recent years with the advent of neural network-based methods, achieving notable improvements on signal-level evaluation metrics. Existing speech separation systems are mostly optimized with signal-level objectives, and often yield outputs that are acceptable in waveform metrics but suboptimal for intelligibility-sensitive downstream tasks. To address this gap, we propose SLM-SS, a token-based generative speech separation framework that models multi-talker mixtures with speech language models. We first discretize clean speech into multi-codebook token sequences with neural codecs and serialize multi-speaker targets via Serialized...
| Authors | Chenda Li, Tianhua Li, Yiming Chen et al. |
|---|---|
| Primary category | cs.SD |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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