Error-Resilient Semantic Communication for Speech Transmission over Packet-Loss Networks
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of Glaris, a generative latent-prior-based framework for resilient speech communication that effectively balances semantic consistency and reconstruction fidelity in the presence of packet loss. The comprehensive methodology and experimental validation demonstrate its potential to advance the field of error-resilient communication systems.
Methodology
The paper introduces Glaris, a novel framework for error-resilient semantic communication in speech transmission over packet-loss networks. It employs a two-stage coding architecture utilizing generative latent priors to enhance both semantic consistency and reconstruction fidelity. The integration of in-band forward error correction (FEC) with packet loss concealment (PLC) is a significant methodological advancement, as it allows for adaptive redundancy control and improved resilience against dynamic channel conditions. The use of a VQ-VAE for encoding high-dimensional speech into a compact latent representation is well-justified and effectively addresses the challenges of maintaining quality under packet loss.
Results & evaluation
The experimental evaluation is thorough, utilizing the LibriSpeech dataset under various packet-loss conditions, including both simulated and real-world scenarios. The results demonstrate that Glaris outperforms existing codecs in terms of robustness and efficiency. The use of multiple metrics (PESQ, STOI, WER, and MOS) provides a comprehensive assessment of performance, and the subjective listening tests further validate the framework's effectiveness. However, the paper could benefit from a more detailed comparison with additional state-of-the-art methods to strengthen the claims of superiority.
Reproducibility
The paper provides a detailed methodology and experimental setup, including training parameters and loss functions, which enhances reproducibility. However, the absence of a publicly available code repository or demo URL limits the ability for others to replicate the results directly. Including such resources would significantly improve the paper's reproducibility.
Limitations
One limitation is the potential trade-off between redundancy and compression efficiency, particularly in low-bitrate scenarios where excessive redundancy may degrade quality. Additionally, while the framework shows promise, its performance in highly variable real-world conditions remains to be fully validated. The reliance on specific datasets may also limit the generalizability of the findings.
Broader impact
The proposed framework has significant implications for real-time speech communication applications, particularly in scenarios where packet loss is common, such as VoIP and online meetings. By improving the robustness and efficiency of speech transmission, Glaris could enhance user experience in various latency-sensitive applications. The integration of semantic communication principles may also pave the way for future advancements in other modalities, such as video and text transmission. The main contribution of this paper is the introduction of Glaris, a generative latent-prior-based framework for resilient speech communication that effectively balances semantic consistency and reconstruction fidelity in the presence of packet loss. The comprehensive methodology and experimental validation demonstrate its potential to advance the field of error-resilient communication systems.
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