A Perceptually Motivated Approach for Low-Complexity Speech Semantic Communication

Deep learning-based semantic communication is an emerging communication method that achieves cooperative transmission between source and channel. The primary objectives of semantic communication are to enhance the efficiency of information transmission and ensure the accurate restoration of semantic content. Recent studies have shown that semantic communication performs well in enhancing transmission rates, especially in low signal-to-noise ratio environments. However, existing speech semantic communication methods neglect to account for speech perception at the receiver and the complexity of the method, which limits the practical implementation of semantic communication methods. In this paper, we propose a perceptually-motivated, low-complexity speech semantic communication method. Specifically, we employ an end-to-end communication approach to transmit the source speech and obtain the reconstructed speech at the receiver. To ensure the accurate extraction of semantic information, we present a low-complexity fully convolutional semantic encoder, which increases the accuracy of semantic information extraction and improves transmission efficiency. Considering the sensitivity of human perception, a multi-resolution joint loss function has been implemented to enhance the model's performance and guarantee that the reconstructed speech aligns with the human ear's auditory perception. Experimental results show that the proposed method performs better on objective and subjective metrics than existing speech transmission methods. Compared with existing neural semantic transmission methods, we improve the transmission efficiency, and the number of symbols needed for transmission is decreased by 60% without compromising the quality of speech. Furthermore, the proposed semantic communication method has a lower complexity and consumes less time to transmit.