UAF: A Unified Audio Front-end LLM for Full-Duplex Speech Interaction
Full-duplex speech interaction, as the most natural and intuitive mode of human communication, is driving artificial intelligence toward more human-like conversational systems. Traditional cascaded speech processing pipelines suffer from critical limitations, including accumulated latency, information loss, and error propagation across modules. To address these issues, recent efforts focus on the end-to-end audio large language models (LLMs) like GPT-4o, which primarily unify speech understanding and generation task. However, most of these models are inherently half-duplex, and rely on a suite of separate, task-specific front-end components, such as voice activity detection (VAD) and turn...
| Authors | Yadong Li, Guoxin Wu, Haiping Hou et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 April 2026 |
| Last updated at source | 30 April 2026 |
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