Semantic-Aware Speech-to-Text Transmission Over MIMO Channels

Semantic communications have been utilized to execute numerous intelligent tasks by transmitting task-related semantic information instead of bits. In this paper, we propose a semantic-aware speech-to-text transmission system over MIMO channels with single-user, 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 text-related semantic features by leveraging the transformer module. Moreover, a novel neural network-enabled semantic-aware network is proposed to facilitate the transmission with high semantic fidelity, which identifies the critical semantic information and guarantees them to be recovered accurately. According to the simulation results, the proposed SAC-ST outperforms the communication framework without the semantic-aware network for speech-to-text transmission over MIMO channels in terms of the speech-to-text metrics, especially in the low signal-to-noise regime.