Modern text-to-speech (TTS) systems are rarely deployed as unchanged pre-trained models. They are often adapted through supervised fine-tuning (SFT) or preference optimization such as DPO and GRPO. This raises a practical question for audio deepfake forensics: do fingerprints learned from a foundation generator remain...
Model intelligence and fast response jointly shape the quality of interaction with speech language models, yet remain difficult to achieve together. Explicit chain-of-thought (CoT) improves reasoning and audio understanding, but generating intermediate reasoning tokens delays responses. Describing fine-grained acoustic...
Yu-Xiang Wang, Kun-Yu Feng, Yuan-Cheng Wang et al.· 0 citations
Audio language models understand what is said far better than how it sounds. Closing this gap takes more than data. Detailed acoustic annotation is costly, labels from stronger models inherit their errors and limits, and fixed data cannot adapt as the learner improves. We therefore propose EvoAudio, a recursive self-im...
Yu-Xiang Wang, Sheng-Bo Cai, Ying Shen et al.· 0 citations
ParaBridge is proposed, an on-policy self-distillation method that turns a brittle inference-time scaffold into stable model behavior and generalizes to unseen paralinguistic cues, transfers from safety-oriented training to empathy-oriented dialogue, and works on a different SLM backbone.
Yuxiang Wang, Qin-Ke Ni, Sheng-Bo Cai et al.· arXiv.org· 2 citations
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