  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.
