Semantic Communication Systems for Speech Transmission

By Zhenzi Weng, Zhijin Qin. IEEE Journal on Selected Areas in Communications, 2021

668 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/108d62c4d1f67304cd7b25412967dd0e5b724245

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Citations in the corpus

Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, positive): “instance in [222], a DL-based semantic communication system was proposed for speech transmission, where the mean-squared error (MSE) was used as the learning goal.”
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications (2022, neutral): “cation system for speech signal reconstruction in [233], which aims to minimize the MSE between the input and recovered speech sequences.”
Resource Allocation for Text Semantic Communications (2022, neutral): “Particularly, different types of semantic systems have been studied for different types of sources, including text [3], [4], image [5], [6], speech [7], and video [8], to ensure signiﬁcant improvement in semantic transmission reliability.”
Semantic Communications: Principles and Challenges (2021, background): “[13] proposed an extension of DeepSC for speech signals, named DeepSC-S.”
Task-Oriented Multi-User Semantic Communications (2021, background): “Weng et al. [14] developed an attention mechanism based semantic communication systems to reconstruct speech signals.”