  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.
