Semantic communications have been utilized to execute numerous intelligent tasks by transmitting task-related semantic information instead of bits. In this article, we propose a semantic-aware speech-to-text transmission system for the single-user multiple-input multiple-output (MIMO) and multi-user MIMO communication scenarios, named SAC-ST. Particularly, a semantic communication system to serve the speech-to-text task at the receiver is first designed, which compresses the semantic information and generates the low-dimensional semantic features by leveraging the transformer module. In addition, a novel semantic-aware network is proposed to facilitate the transmission with high semantic fidelity to identify the critical semantic information and guarantee it is recovered accurately. Furthermore, we extend the SAC-ST with a neural network-enabled channel estimation network to mitigate the dependence on accurate channel state information and validate the feasibility of SAC-ST in practical communication environments. Simulation results will show that the proposed SAC-ST outperforms the communication framework without the semantic-aware network for speech-to-text transmission over the MIMO channels in terms of the speech-to-text metrics, especially in the low signal-to-noise regime. Moreover, the SAC-ST with the developed channel estimation network is comparable to the SAC-ST with perfect channel state information.

翻译：语义通信通过传输与任务相关的语义信息而非比特，已被用于执行多种智能任务。本文针对单用户多输入多输出（MIMO）和多用户MIMO通信场景，提出了一种名为SAC-ST的语义感知语音到文本传输系统。具体而言，首先设计了一种服务于接收端语音到文本任务的语义通信系统，该系统利用Transformer模块压缩语义信息并生成低维语义特征。此外，提出了一种新型语义感知网络，通过识别关键语义信息并确保其准确恢复，以高语义保真度促进传输。进一步地，我们将SAC-ST与基于神经网络的信道估计网络相结合，以减轻对精确信道状态信息的依赖，并验证SAC-ST在实际通信环境中的可行性。仿真结果表明，所提出的SAC-ST在语音到文本指标上优于未采用语义感知网络的MIMO信道语音到文本传输通信框架，尤其在低信噪比条件下表现突出。此外，结合所开发信道估计网络的SAC-ST与具有完美信道状态信息的SAC-ST性能相当。