1 citations · 1 across the 5 of their papers we have counts for
6 papers · 1 filter
Decoupling Conversational Dynamics in Full-Duplex Spoken Models through Reinforcement Learning
Yuxin Li, Donghang Wu, Guan-Ting Lin +4
Recent full-duplex spoken dialogue models have demonstrated compelling progress toward human-like interaction, enabling agents to respond with low latency, produce backchannels, an…
The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning
Donghang Wu, Tianyu Zhang, Yuxin Li +4
During conversational interactions, humans subconsciously engage in concurrent thinking while listening to a speaker. Although this internal cognitive processing may not always man…
Full-Duplex-Bench-v3: Benchmarking Tool Use for Full-Duplex Voice Agents Under Real-World Disfluency
Guan-Ting Lin, Chen Chen, Zhehuai Chen +1
We introduce Full-Duplex-Bench-v3 (FDB-v3), a benchmark for evaluating spoken language models under naturalistic speech conditions and multi-step tool use. Unlike prior work, our d…
From KAN to GR-KAN: Advancing Speech Enhancement with KAN-Based Methodology
Haoyang Li, Yuchen Hu, Chen Chen +3
Deep neural network (DNN)-based speech enhancement (SE) usually uses conventional activation functions, which lack the expressiveness to capture complex multiscale structures neede…
GenSE: Generative Speech Enhancement via Language Models using Hierarchical Modeling
Jixun Yao, Hexin Liu, Chen Chen +3
Semantic information refers to the meaning conveyed through words, phrases, and contextual relationships within a given linguistic structure. Humans can leverage semantic informati…
SSR-Speech: Towards Stable, Safe and Robust Zero-shot Text-based Speech Editing and Synthesis
Helin Wang, Meng Yu, Jiarui Hai +5
In this paper, we introduce SSR-Speech, a neural codec autoregressive model designed for stable, safe, and robust zero-shot textbased speech editing and text-to-speech synthesis. S…