We consider a semantic communication system for speech signals, named DeepSC-S. Motivated by the breakthroughs in deep learning (DL), we make an effort to recover the transmitted speech signals in the semantic communication systems, which minimizes the error at the semantic level rather than the bit level or symbol level as in the traditional communication systems. Particularly, based on an attention mechanism employing squeeze-and-excitation (SE) networks, we design the transceiver as an end-to-end (E2E) system, which learns and extracts the essential speech information. Furthermore, in order to facilitate the proposed DeepSC-S to work well on dynamic practical communication scenarios, we find a model yielding good performance when coping with various channel environments without retraining process. The simulation results demonstrate that our proposed DeepSC-S is more robust to channel variations and outperforms the traditional communication systems, especially in the low signal-to-noise (SNR) regime.
翻译:我们考虑的是语音信号的语义通信系统,名为DeepSC-S。 受深层次学习(DL)突破的驱动,我们努力恢复语义通信系统中传输的语音信号,这在语义通信系统中将错误降到最低程度,而不是传统通信系统中的比特级别或符号级别。 特别是,我们根据使用挤压和抽查(SE)网络的注意机制,设计收发器为端到端系统,学习和提取基本的语音信息。 此外,为了便利拟议的深层SC-S在动态实用通信情景上很好地工作,我们发现一种模型,在不经过再培训处理各种频道环境时产生良好的性能。 模拟结果表明,我们提议的深层SC-S更能引导变异,超越传统通信系统,特别是在低信号到噪音(SNR)制度下。