SNR-Adaptive Multi-Layer Semantic Communication for Speech

Deep learning (DL) enabled semantic communication has been demonstrated to be efficient in speech transmission under fixed channels by equivalently transmitting all the reconstruction-related semantic features, which however, neglects the effects of distinct semantic features under dynamic channels, constraining the further improvement of communication efficiency. In this paper, we propose a signal-to-noise ratio (SNR)-adaptive multi-layer joint semantic-channel coding framework for speech transmission. Specifically, to achieve the consistency between the source speeches and the reconstructed ones at the semantic level, a multi-layer semantic representation framework is specially designed for speeches, where not only the content-related semantic feature, but also the acoustic-related semantic features are extracted to combat the semantic distortion caused by channel noise and attenuation. By jointly considering the effect of different semantic features and the dynamic channel conditions, an SNR-adaptive channel encoder is proposed to fuse the multi-layer semantic features, where an attention-based gating network is adopted to adjust the proportion of fusion with aim of efficiency maximization. Simulation results show that the proposed system can significantly improve the communication efficiency and robustness under dynamic SNRs.