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.

该研究通过设计一种基于深度学习和注意机制的语音语义通信系统 DeepSC-S，以提高在噪音环境下的语音信号精度和鲁棒性，并在电话和多媒体传输系统中进行了实践验证。研究证明 DeepSC-S 在语音信号指标方面优于传统通信方法，并且在低信噪比环境中表现更加出色，这可以通过提供更高的权重来识别必要的语音信息。