PhoenixCodec: Taming Neural Speech Coding for Extreme Low-Resource Scenarios
Abstract
This paper presents PhoenixCodec, a comprehensive neural speech coding and decoding framework designed for extremely low-resource conditions. The proposed system integrates an optimized asymmetric frequency-time architecture, a Cyclical Calibration and Refinement (CCR) training strategy, and a noise-invariant fine-tuning procedure. Under stringent constraints - computation below 700 MFLOPs, latency less than 30 ms, and dual-rate support at 1 kbps and 6 kbps - existing methods face a trade-off between efficiency and quality. PhoenixCodec addresses these challenges by alleviating the resource scattering of conventional decoders, employing CCR to enhance optimization stability, and enhancing robustness through noisy-sample fine-tuning. In the LRAC 2025 Challenge Track 1, the proposed system ranked third overall and demonstrated the best performance at 1 kbps in both real-world noise and reverberation and intelligibility in clean tests, confirming its effectiveness.
Cite
@article{arxiv.2510.21196,
title = {PhoenixCodec: Taming Neural Speech Coding for Extreme Low-Resource Scenarios},
author = {Zixiang Wan and Haoran Zhao and Guochang Zhang and Runqiang Han and Jianqiang Wei and Yuexian Zou},
journal= {arXiv preprint arXiv:2510.21196},
year = {2026}
}
Comments
Accepted by ICASSP 2026; 5 pages, 1 figure, 4 tables