  Semantic communications could improve the transmission efficiency
significantly by exploring the semantic information. In this paper, 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 or
symbol level. Particularly, we design a deep learning (DL)-enabled semantic
communication system for speech signals, named DeepSC-S. In order to improve
the recovery accuracy of speech signals, especially for the essential
information, DeepSC-S is developed based on an attention mechanism by utilizing
a squeeze-and-excitation (SE) network. The motivation behind the attention
mechanism is to identify the essential speech information by providing higher
weights to them when training the neural network. Moreover, in order to
facilitate the proposed DeepSC-S for dynamic channel environments, we find a
general model to cope with various channel conditions without retraining.
Furthermore, we investigate DeepSC-S in telephone systems as well as multimedia
transmission systems to verify the model adaptation in practice. The simulation
results demonstrate that our proposed DeepSC-S outperforms the traditional
communications in both cases in terms of the speech signals metrics, such as
signal-to-distortion ration and perceptual evaluation of speech distortion.
Besides, DeepSC-S is more robust to channel variations, especially in the low
signal-to-noise (SNR) regime.
