Robust Semantic Communications For Speech Transmission
2024 Β· Zhenzi Weng, Zhijin Qin, Geoffrey Ye Li
Abstract
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 impro
Authors
(none)
Tags
Stats
Related papers
- Semantic Communications For Speech Signals (2020)14.35
- Deep Learning Enabled Semantic Communications With Speech Recognition And Synthesis (2022)17.85
- Semantic Communications For Speech Recognition (2021)11.93
- Textless Streaming Speech-to-speech Translation Using Semantic Speech Tokens (2024)3.58
- Utilizing Neural Transducers For Two-stage Text-to-speech Via Semantic Token Prediction (2024)0.00
- Socodec: A Semantic-ordered Multi-stream Speech Codec For Efficient Language Model Based Text-to-speech Synthesis (2024)6.34
- Telephonetic: Making Neural Language Models Robust To ASR And Semantic Noise (2019)0.00
- Large Generative Model-assisted Talking-face Semantic Communication System (2024)5.84