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.

中文翻译：

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用于语音传输的语义通信系统


语义通信通过挖掘语义信息可以显着提高传输效率。在本文中，我们努力恢复语义通信系统中传输的语音信号，从而最大限度地减少语义级别而不是比特或符号级别的错误。特别是，我们设计了一种支持深度学习（DL）的语音信号语义通信系统，名为 DeepSC-S。为了提高语音信号尤其是关键信息的恢复精度，DeepSC-S是利用挤压和激励（SE）网络基于注意力机制开发的。注意力机制背后的动机是在训练神经网络时通过为其提供更高的权重来识别基本的语音信息。此外，为了促进所提出的 DeepSC-S 适用于动态信道环境，我们找到了一个通用模型来应对各种信道条件而无需重新训练。此外，我们研究了电话系统和多媒体传输系统中的 DeepSC-S，以在实践中验证模型的适应性。仿真结果表明，我们提出的 DeepSC-S 在两种情况下在语音信号指标（例如信号失真比和语音失真的感知评估）方面均优于传统通信。此外，DeepSC-S 对信道变化的鲁棒性更强，尤其是在低信噪比 (SNR) 情况下。