The rapid development of generative Artificial Intelligence (AI) continually unveils the potential of Semantic Communication (SemCom). However, current talking-face SemCom systems still encounter challenges such as low bandwidth utilization, semantic ambiguity, and diminished Quality of Experience (QoE). This study introduces a Large Generative Model-assisted Talking-face Semantic Communication (LGM-TSC) System tailored for the talking-face video communication. Firstly, we introduce a Generative Semantic Extractor (GSE) at the transmitter based on the FunASR model to convert semantically sparse talking-face videos into texts with high information density. Secondly, we establish a private Knowledge Base (KB) based on the Large Language Model (LLM) for semantic disambiguation and correction, complemented by a joint knowledge base-semantic-channel coding scheme. Finally, at the receiver, we propose a Generative Semantic Reconstructor (GSR) that utilizes BERT-VITS2 and SadTalker models to transform text back into a high-QoE talking-face video matching the user's timbre. Simulation results demonstrate the feasibility and effectiveness of the proposed LGM-TSC system.

翻译：生成式人工智能（Generative AI）的快速发展不断揭示语义通信（SemCom）的潜力。然而，当前面向说话人脸的语义通信系统仍面临带宽利用率低、语义模糊以及体验质量（QoE）下降等挑战。本研究针对说话人脸视频通信，提出了一种大型生成模型辅助的说话人脸语义通信（LGM-TSC）系统。首先，我们在发送端基于FunASR模型引入一个生成式语义提取器（GSE），将语义稀疏的说话人脸视频转换为高信息密度的文本。其次，我们基于大语言模型（LLM）建立了一个私有知识库（KB），用于语义消歧与校正，并辅以一个联合知识库-语义信道编码方案。最后，在接收端，我们提出了一种生成式语义重建器（GSR），它利用BERT-VITS2和SadTalker模型将文本转换回与用户音色匹配的高QoE说话人脸视频。仿真结果验证了所提出的LGM-TSC系统的可行性与有效性。