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cs.SD2026
What Matters in Preference Post-Training of Continuous Autoregressive Speech Language Models
Yingda Shen, Yao Qian, Dekun Chen +6
Continuous autoregressive speech language models combine an autoregressive (AR) backbone with a diffusion or flow-matching head. Preference post-training requires choosing how to c…
cs.SD2026
AuraSE: Low-Hallucination Generative Speech Enhancement via Multimodal Flow Matching and Inference Policy Optimization
Yingda Shen, Yao Qian, Yuxuan Hu +7
Generative speech enhancement models can produce cleaner and more natural-sounding speech than conventional discriminative approaches, but may hallucinate by changing speech conten…
cs.SD2026
Interpreting and Evaluating Dynamic-Rate Speech Codec Boundaries
Han Wang, Jiaqi Li, Yingda Shen +2
Dynamic-frame-rate neural speech codecs replace a uniform frame grid with variable-duration tokens, making boundary placement part of the representation itself. Yet it is unclear w…