Semantic Communication Systems for Speech Transmission

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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE journal on selected areas in communications Ročník 39; číslo 8; s. 2434 - 2444
Hlavní autoři: Weng, Zhenzi, Qin, Zhijin
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation models
Communication
Communication systems
Communications systems
Deep learning
Distortion
Multimedia
Multimedia systems
Neural networks
semantic communication
Semantics
Signal to noise ratio
Simulation
Speech
speech transmission
squeeze-and-excitation networks
Telephone sets
Telephone systems
Training
Transmission efficiency
ISSN:0733-8716, 1558-0008
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2021.3087240