用流匹配在特征层优化语音识别，提升噪声下的识别准确率。
Latent-Level Enhancement with Flow Matching for Robust Automatic Speech Recognition
- 在预训练语音识别模型的隐空间中，用流匹配方法修复噪声干扰的特征表示。
- 在多种噪声场景下，词错误率降低，比传统波形增强更稳定有效。
- 可无缝接入现有系统，无需重新训练，适合实际部署场景。
噪声鲁棒自动语音识别（ASR）通常在波形层面应用语音增强（SE），但语音级增强并不总能带来一致的识别性能提升，因残留失真和与ASR编码器隐空间不匹配所致。本文提出一种互补策略——隐空间增强，在ASR推理阶段对失真的表示进行修正。具体设计了一种即插即用的流匹配精炼模块（FM-Refiner），作用于预训练的基于CTC的ASR编码器输出的隐状态。该模块通过学习将不完美隐状态（来自噪声输入或增强后仍有瑕疵的语音）映射至对应干净版本，仅在推理时使用，无需微调ASR参数。实验表明，无论直接作用于噪声输入，还是与传统SE前端结合，FM-Refiner均能持续降低词错误率。结果证明，基于流匹配的隐空间精炼为现有语音增强方法提供了一种轻量、有效的补充方案。
原文摘要 · Abstract (English)
Noise-robust automatic speech recognition (ASR) has been commonly addressed by applying speech enhancement (SE) at the waveform level before recognition. However, speech-level enhancement does not always translate into consistent recognition improvements due to residual distortions and mismatches with the latent space of the ASR encoder. In this letter, we introduce a complementary strategy termed latent-level enhancement, where distorted representations are refined during ASR inference. Specifically, we propose a plug-and-play Flow Matching Refinement module (FM-Refiner) that operates on the output latents of a pretrained CTC-based ASR encoder. Trained to map imperfect latents-either directly from noisy inputs or from enhanced-but-imperfect speech-toward their clean counterparts, the FM-Refiner is applied only at inference, without fine-tuning ASR parameters. Experiments show that FM-Refiner consistently reduces word error rate, both when directly applied to noisy inputs and when combined with conventional SE front-ends. These results demonstrate that latent-level refinement via flow matching provides a lightweight and effective complement to existing SE approaches for robust ASR.
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