Semantic MIMO Systems for Speech-to-Text Transmission
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909336757862400 |
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| author | Weng, Zhenzi Qin, Zhijin Xie, Huiqiang Tao, Xiaoming Letaief, Khaled B. |
| author_facet | Weng, Zhenzi Qin, Zhijin Xie, Huiqiang Tao, Xiaoming Letaief, Khaled B. |
| contents | Semantic communications have been utilized to execute numerous intelligent tasks by transmitting task-related semantic information instead of bits. In this article, we propose a semantic-aware speech-to-text transmission system for the single-user multiple-input multiple-output (MIMO) and multi-user MIMO communication scenarios, named SAC-ST. Particularly, a semantic communication system to serve the speech-to-text task at the receiver is first designed, which compresses the semantic information and generates the low-dimensional semantic features by leveraging the transformer module. In addition, a novel semantic-aware network is proposed to facilitate transmission with high semantic fidelity by identifying the critical semantic information and guaranteeing its accurate recovery. Furthermore, we extend the SAC-ST with a neural network-enabled channel estimation network to mitigate the dependence on accurate channel state information and validate the feasibility of SAC-ST in practical communication environments. Simulation results will show that the proposed SAC-ST outperforms the communication framework without the semantic-aware network for speech-to-text transmission over the MIMO channels in terms of the speech-to-text metrics, especially in the low signal-to-noise regime. Moreover, the SAC-ST with the developed channel estimation network is comparable to the SAC-ST with perfect channel state information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08096 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semantic MIMO Systems for Speech-to-Text Transmission Weng, Zhenzi Qin, Zhijin Xie, Huiqiang Tao, Xiaoming Letaief, Khaled B. Audio and Speech Processing Sound Semantic communications have been utilized to execute numerous intelligent tasks by transmitting task-related semantic information instead of bits. In this article, we propose a semantic-aware speech-to-text transmission system for the single-user multiple-input multiple-output (MIMO) and multi-user MIMO communication scenarios, named SAC-ST. Particularly, a semantic communication system to serve the speech-to-text task at the receiver is first designed, which compresses the semantic information and generates the low-dimensional semantic features by leveraging the transformer module. In addition, a novel semantic-aware network is proposed to facilitate transmission with high semantic fidelity by identifying the critical semantic information and guaranteeing its accurate recovery. Furthermore, we extend the SAC-ST with a neural network-enabled channel estimation network to mitigate the dependence on accurate channel state information and validate the feasibility of SAC-ST in practical communication environments. Simulation results will show that the proposed SAC-ST outperforms the communication framework without the semantic-aware network for speech-to-text transmission over the MIMO channels in terms of the speech-to-text metrics, especially in the low signal-to-noise regime. Moreover, the SAC-ST with the developed channel estimation network is comparable to the SAC-ST with perfect channel state information. |
| title | Semantic MIMO Systems for Speech-to-Text Transmission |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2405.08096 |