# PhoenixCodec: Taming Neural Speech Coding for Extreme Low-Resource Scenarios

arXiv: https://arxiv.org/abs/2510.21196 · HTML: https://mlsanity.com/paper/2510.21196/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 24 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [ReLMCodec: Designing Predictable Speech Tokens from Pre-Quantization Phoneme Structure](https://mlsanity.com/paper/2608.08286/) (2026)

## Abstract (arXiv)

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.