# Semantic Communication Systems for Speech Transmission

arXiv: https://arxiv.org/abs/2102.12605 · HTML: https://mlsanity.com/paper/2102.12605/

## Key facts

- Cited by 97 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2021.

## Built on by (most cited first, 30 of 97)

- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [Robust Semantic Communications with Masked VQ-VAE Enabled Codebook](https://mlsanity.com/paper/2206.04011/) (2022)
- [Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data](https://mlsanity.com/paper/2205.00271/) (2022)
- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems](https://mlsanity.com/paper/2112.10948/) (2021)
- [Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach](https://mlsanity.com/paper/2208.08239/) (2022)
- [Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects](https://mlsanity.com/paper/2212.09668/) (2022)
- [Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints](https://mlsanity.com/paper/2407.18468/) (2024)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2406.07213/) (2024)
- [Vector Quantized Semantic Communication System](https://mlsanity.com/paper/2209.11519/) (2022)
- [Wireless End-to-End Image Transmission System using Semantic Communications](https://mlsanity.com/paper/2302.13721/) (2023)
- [Digital-SC: Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization](https://mlsanity.com/paper/2305.13553/) (2023)
- [Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noises](https://mlsanity.com/paper/2406.06644/) (2024)
- [Cognitive Semantic Communication Systems Driven by Knowledge Graph](https://mlsanity.com/paper/2202.11958/) (2022)
- [Latent Semantic Diffusion-based Channel Adaptive De-Noising SemCom for Future 6G Systems](https://mlsanity.com/paper/2304.09420/) (2023)
- [Demo: Real-Time Semantic Communications with a Vision Transformer](https://mlsanity.com/paper/2205.03886/) (2022)
- [Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks](https://mlsanity.com/paper/2212.10438/) (2022)
- [Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks](https://mlsanity.com/paper/2212.11205/) (2022)
- [Adaptive Resource Allocation for Semantic Communication Networks](https://mlsanity.com/paper/2312.01081/) (2023)
- [Pragmatic Communication in Multi-Agent Collaborative Perception](https://mlsanity.com/paper/2401.12694/) (2024)
- [Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text](https://mlsanity.com/paper/2106.14014/) (2021)
- [Age of Information in Deep Learning-Driven Task-Oriented Communications](https://mlsanity.com/paper/2301.04298/) (2023)
- [Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework](https://mlsanity.com/paper/2306.11229/) (2023)
- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey](https://mlsanity.com/paper/2309.15467/) (2023)
- [One-to-Many Semantic Communication Systems: Design, Implementation, Performance Evaluation](https://mlsanity.com/paper/2209.09425/) (2022)
- [Importance-Aware Image Segmentation-based Semantic Communication for Autonomous Driving](https://mlsanity.com/paper/2401.10153/) (2024)
- [Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck](https://mlsanity.com/paper/2405.09514/) (2024)

## Abstract (arXiv)

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.