Adaptive Semantic Speech Transmission for High-Speed Scenarios

The fast time-varying channels in high-speed scenarios impact signal transmission between transceivers and pose challenges to both the accuracy and bandwidth utilization of communication systems. Semantic communication, known for its ability to significantly reduce transmission bandwidth and enhance communication reliability, is especially effective in extreme environments. However, current semantic communication systems lack a comprehensive physical layer design, which limits their ability to achieve optimal performance in rapidly changing conditions. In this paper, we propose an adaptive semantic speech recognition and cloning transmission system with a superimposed pilot (SwitchAC-SIP) tailored for high-speed scenarios to ensure high-quality speech transmission. The system converts speech signals into textual content and speaker timbre features at the transmitter, while a speech cloning model reconstructs the speech at the receiver with a timbre closely resembling the original speaker based on these features, thereby eliminating the need to retrain the speech generation model for different users, ensuring both transmission quality and efficiency. To address the impact of high-speed environments on channel estimation performance, we introduce a superimposed pilot (SIP) in the physical layer. This method superimposes pilots and data across the entire time-frequency grid with a specific power ratio, significantly mitigating the detrimental effects of high-speed conditions on semantic communication systems. Furthermore, to enhance system flexibility in dynamic scenarios, we design a channel-adaptive network that dynamically allocates bandwidth ratios for text and audio semantics based on real-time channel conditions. This adaptive approach prioritizes the protection of critical semantic features according to user requirements. Simulation results demonstrate the substantial improvements in transmission efficiency and accuracy achieved by the proposed system.