# Robust Semantic Communications for Speech Transmission

arXiv: https://arxiv.org/abs/2403.05187 · HTML: https://mlsanity.com/paper/2403.05187/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2024.

## Built on by (most cited first, 2 of 2)

- [World Model-Enabled Causal Digital Twins for Semantic Communications in Physical AI Systems](https://mlsanity.com/paper/2605.16547/) (2026)
- [Distributionally Robust Wireless Semantic Communication with Large AI Models](https://mlsanity.com/paper/2506.03167/) (2025)

## Abstract (arXiv)

In this paper, we propose a robust semantic communication system for speech transmission, named Ross-S2T, by delivering the essential semantic information. Specifically, we consider the speech-to-text translation (S2TT) as the transmission goal. First, a new deep semantic encoder is developed to convert speech in the source language to textual features associated with the target language, facilitating the end-to-end semantic exchange to perform the S2TT task and reducing the transmission data without performance degradation. To mitigate semantic impairments inherent in the corrupted speech, a novel generative adversarial network (GAN)-enabled deep semantic compensator is established to estimate the lost semantic information within the speech and extract deep semantic features simultaneously, which enables robust semantic transmission for corrupted speech. Furthermore, a semantic probe-aided compensator is devised to enhance the semantic fidelity of recovered semantic features and improve the understandability of the target text. According to simulation results, the proposed Ross-S2T exhibits superior S2TT performance compared to conventional approaches and high robustness against semantic impairments.