Large Generative Model-assisted Talking-face Semantic Communication System

• URL: http://arxiv.org/abs/2411.03876v1
• Date: Wed, 06 Nov 2024 12:45:46 GMT
• Title: Large Generative Model-assisted Talking-face Semantic Communication System
• Authors: Feibo Jiang, Siwei Tu, Li Dong, Cunhua Pan, Jiangzhou Wang, Xiaohu You,
• Abstract summary: This study introduces a Large Generative Model-assisted Talking-face Semantic Communication (LGM-TSC) system. Generative Semantic Extractor (GSE) at the transmitter converts semantically sparse talking-face videos into texts with high information density. Private Knowledge Base (KB) based on the Large Language Model (LLM) for semantic disambiguation and correction. Generative Semantic Reconstructor (GSR) that utilizes BERT-VITS2 and SadTalker models to transform text back into a high-QoE talking-face video.
• Score: 55.42631520122753
• License:
• Abstract: 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.

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