论文标题
语音信号的语义通信
Semantic Communications for Speech Signals
论文作者
论文摘要
我们考虑语音信号的语义通信系统，名为DeepSc-s。在深度学习（DL）的突破性的推动下，我们努力恢复语义通信系统中传播的语音信号，该信号将语义级别的错误最小化，而不是像传统通信系统中的位级别或符号级别。特别是，基于采用挤压和兴奋（SE）网络的注意机制，我们将收发器设计为端到端（E2E）系统，该系统可以学习和提取基本的语音信息。此外，为了促进拟议的DeepSc-S以在动态实践通信方案上很好地工作，我们发现在应对各种频道环境而无需重新训练过程时，可以产生良好的性能。仿真结果表明，我们提出的DEEPSC-S对于通道变化和表现要优于传统通信系统更强大，尤其是在低信噪比（SNR）方面。
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.
