Semantic Communications for Speech Signals

We consider a semantic communication system for speech signals, named DeepSC-S. Motivated by the break-throughs 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.

Automated summary

The researchers proposed a semantic communication system named DeepSC-S, leveraging breakthroughs in deep learning, aimed at minimizing errors at the semantic level when recovering transmitted speech signals. They designed an end-to-end system with an attention mechanism utilizing squeeze-and-excitation networks to learn and extract essential speech information. Simulation results showed that the system is more robust to channel variations and outperforms traditional communication systems in low signal-to-noise environments.