MAGE通过分步聚焦高频低频词提升语音增强效率与质量。
MAGE: A Coarse-to-Fine Speech Enhancer with Masked Generative Model
- 分阶段掩码：先处理常见语音片段，再细化稀有片段。
- 200M参数模型在DNS和LibriSpeech上表现超大模型，WER显著降低。
- 适合追求高效高质语音增强的工业应用开发者。
语音增强面临效率与感知质量之间的权衡难题。本文提出MAGE，一种基于掩码生成机制的紧凑鲁棒语音增强框架。不同于以往随机掩码方法，MAGE采用稀缺性感知的粗到细掩码策略，在早期步骤优先处理高频语音单元，后期聚焦稀有单元，从而提升效率与泛化能力。同时引入轻量级修正模块，检测低置信度预测并重新掩码以进一步稳定推理。模型基于BigCodec构建，并在Qwen2.5-0.5B基础上微调，通过选择性保留层压缩至200M参数。在DNS Challenge和噪声版LibriSpeech数据集上的实验表明，MAGE在感知质量上达到当前最优水平，并显著降低下游语音识别的词错误率，优于多个更大规模基线模型。音频示例可访问https://hieugiaosu.github.io/MAGE/。
原文摘要 · Abstract (English)
Speech enhancement remains challenging due to the trade-off between efficiency and perceptual quality. In this paper, we introduce MAGE, a Masked Audio Generative Enhancer that advances generative speech enhancement through a compact and robust design. Unlike prior masked generative models with random masking, MAGE employs a scarcity-aware coarse-to-fine masking strategy that prioritizes frequent tokens in early steps and rare tokens in later refinements, improving efficiency and generalization. We also propose a lightweight corrector module that further stabilizes inference by detecting low-confidence predictions and re-masking them for refinement. Built on BigCodec and finetuned from Qwen2.5-0.5B, MAGE is reduced to 200M parameters through selective layer retention. Experiments on DNS Challenge and noisy LibriSpeech show that MAGE achieves state-of-the-art perceptual quality and significantly reduces word error rate for downstream recognition, outperforming larger baselines. Audio examples are available at https://hieugiaosu.github.io/MAGE/.
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