面向文本到语音合成的生成式语义通信
Generative Semantic Communication for Text-to-Speech Synthesis
AI总结：
本文提出一种面向文本到语音合成的生成式语义通信框架，利用WavLM、残差向量量化、Transformer编码器和扩散模型构建收发端语义知识库并实现高效编码，在多种信道下生成语音保真度显著优于四个基线。
摘要：
语义通信是一种通过仅传输源数据的语义信息来提高通信效率的有前景的技术。然而，传统的语义通信方法主要关注数据重建任务，这对于文本到语音（TTS）合成等新兴生成任务而言可能并不高效。为了解决这一局限，本文利用生成式人工智能技术，开发了一种新颖的面向TTS合成的生成式语义通信框架。首先，我们利用预训练的大型语音模型WavLM和残差向量量化方法，分别在发射机和接收机构建两个语义知识库（KB）。发射机处的知识库支持有效的语义提取，而接收机处的知识库则有助于实现逼真的语音合成。然后，我们采用Transformer编码器和扩散模型来实现高效的语义编码，而不会引入显著的通信开销。最后，数值结果表明，在加性高斯白噪声信道和瑞利衰落信道两种情况下，我们的框架所生成语音的保真度均显著高于四个基线方法。
英文摘要：
Semantic communication is a promising technology to improve communication efficiency by transmitting only the semantic information of the source data. However, traditional semantic communication methods primarily focus on data reconstruction tasks, which may not be efficient for emerging generative tasks such as text-to-speech (TTS) synthesis. To address this limitation, this paper develops a novel generative semantic communication framework for TTS synthesis, leveraging generative artificial intelligence technologies. Firstly, we utilize a pre-trained large speech model called WavLM and the residual vector quantization method to construct two semantic knowledge bases (KBs) at the transmitter and receiver, respectively. The KB at the transmitter enables effective semantic extraction, while the KB at the receiver facilitates lifelike speech synthesis. Then, we employ a transformer encoder and a diffusion model to achieve efficient semantic coding without introducing significant communication overhead. Finally, numerical results demonstrate that our framework achieves much higher fidelity for the generated speech than four baselines, in both cases with additive white Gaussian noise channel and Rayleigh fading channel.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
