Error-Resilient Semantic Communication for Speech Transmission over Packet-Loss Networks

TL;DR

This paper introduces Glaris, a novel semantic communication framework that enhances speech transmission robustness over packet-loss networks by using generative latent priors for high-quality packet loss concealment and error resilience.

Contribution

Glaris is the first to integrate generative latent priors into semantic speech communication for improved error resilience and compatibility with existing digital systems.

Findings

Glaris outperforms traditional FEC in robustness and efficiency.

It maintains semantic and reconstruction quality under high packet loss.

It reduces redundancy overhead compared to existing methods.

Abstract

Real-time speech communication over wireless networks remains challenging, as conventional channel protection mechanisms cannot effectively counter packet loss under stringent bandwidth and latency constraints. Semantic communication has emerged as a promising paradigm for enhancing the robustness of speech transmission by means of joint source-channel coding (JSCC). However, its cross-layer design hinders practical deployment due to the incompatibility with existing digital communication systems. In this case, the robustness of speech communication is consequently evaluated primarily by the error-resilience to packet loss over wireless networks. To address these challenges, we propose \emph{Glaris}, a generative latent-prior-based resilient speech semantic communication framework that performs resilient speech coding in the generative latent space. Generative latent priors enable high-quality packet loss concealment (PLC) at the receiver side, well-balancing semantic consistency and reconstruction fidelity. Additionally, an integrated error resilience mechanism is designed to mitigate the error propagation and improve the effectiveness of PLC. Compared with traditional packet-level forward error correction (FEC) strategies, our new method achieves enhanced robustness over dynamic wireless networks while reducing redundancy overhead significantly. Experimental results on the LibriSpeech dataset demonstrate that \emph{Glaris} consistently outperforms existing error-resilient codecs, achieving JSCC-level robustness while maintaining seamless compatibility with existing systems, and it also strikes a favorable balance between transmission efficiency and speech reconstruction quality.