  Semantic communications could improve the transmission efficiency
significantly by exploring the input semantic information. 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, we design a
DL-enabled semantic communication system for speech signals, named DeepSC-S.
Based on an attention mechanism employing squeeze-and-excitation (SE) networks,
DeepSC-S is able to identify the essential speech information and assign high
values to the weights corresponding to the essential information when training
the neural network. Moreover, in order to facilitate the proposed DeepSC-S to
cater to dynamic channel environments, we dedicate to find a general model to
cope with various channel conditions without retraining. Furthermore, to verify
the model adaptation in practice, we investigate DeepSC-S in the telephone
systems as well as the multimedia transmission systems, which usually requires
higher data rates and lower transmission latency. The simulation results
demonstrate that our proposed DeepSC-S achieves higher system performance than
the traditional communications in both telephone systems and multimedia
transmission systems by comparing the speech signals metrics,
signal-to-distortion ration and perceptual evaluation of speech distortion.
Besides, DeepSC-S is more robust to channel variations than the traditional
approaches, especially in the low signal-to-noise (SNR) regime.
