Semantic Communication Systems for Speech Transmission

TL;DR

This paper introduces DeepSC-S, a deep learning-based semantic communication system for speech transmission that emphasizes semantic-level accuracy, utilizing attention mechanisms to enhance essential information recovery and robustness across varying channel conditions.

Contribution

The paper presents a novel deep learning semantic communication system for speech, incorporating attention mechanisms and a general model for dynamic channels, outperforming traditional methods.

Findings

DeepSC-S outperforms traditional communication systems in speech quality metrics.

The system is robust to channel variations, especially at low SNR.

DeepSC-S effectively captures essential speech information using attention mechanisms.

Abstract

Semantic communications could improve the transmission efficiency significantly by exploring the semantic information. In this paper, we make an effort to recover the transmitted speech signals in the semantic communication systems, which minimizes the error at the semantic level rather than the bit or symbol level. Particularly, we design a deep learning (DL)-enabled semantic communication system for speech signals, named DeepSC-S. In order to improve the recovery accuracy of speech signals, especially for the essential information, DeepSC-S is developed based on an attention mechanism by utilizing a squeeze-and-excitation (SE) network. The motivation behind the attention mechanism is to identify the essential speech information by providing higher weights to them when training the neural network. Moreover, in order to facilitate the proposed DeepSC-S for dynamic channel environments, we find a general model to cope with various channel conditions without retraining. Furthermore, we investigate DeepSC-S in telephone systems as well as multimedia transmission systems to verify the model adaptation in practice. The simulation results demonstrate that our proposed DeepSC-S outperforms the traditional communications in both cases in terms of the speech signals metrics, such as signal-to-distortion ration and perceptual evaluation of speech distortion. Besides, DeepSC-S is more robust to channel variations, especially in the low signal-to-noise (SNR) regime.