# Semantic Communications for Speech Signals

arXiv: https://arxiv.org/abs/2012.05369 · HTML: https://mlsanity.com/paper/2012.05369/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 19 of 19)

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- [Training-Free Multi-User Generative Semantic Communications via Null-Space Diffusion Sampling](https://mlsanity.com/paper/2405.09866/) (2024)
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- [Economics of Semantic Communication System: An Auction Approach](https://mlsanity.com/paper/2208.05040/) (2022)
- [Resource-Efficient Multiview Perception: Integrating Semantic Masking with Masked Autoencoders](https://mlsanity.com/paper/2410.04817/) (2024)
- [Enhancing Semantic Communication with Deep Generative Models -- An ICASSP Special Session Overview](https://mlsanity.com/paper/2309.02478/) (2023)
- [Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels](https://mlsanity.com/paper/2101.10369/) (2021)
- [Integrating Semantic Communication and Human Decision-Making into an End-to-End Sensing-Decision Framework](https://mlsanity.com/paper/2412.05103/) (2024)

## Abstract (arXiv)

We consider a semantic communication system for speech signals, named DeepSC-S. Motivated by the breakthroughs in deep learning (DL), 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 level or symbol level as in the traditional communication systems. Particularly, based on an attention mechanism employing squeeze-and-excitation (SE) networks, we design the transceiver as an end-to-end (E2E) system, which learns and extracts the essential speech information. Furthermore, in order to facilitate the proposed DeepSC-S to work well on dynamic practical communication scenarios, we find a model yielding good performance when coping with various channel environments without retraining process. The simulation results demonstrate that our proposed DeepSC-S is more robust to channel variations and outperforms the traditional communication systems, especially in the low signal-to-noise (SNR) regime.