Latent-Level Enhancement with Flow Matching for Robust Automatic Speech Recognition
Abstract
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.
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
This paper presents a significant advancement in noise-robust automatic speech recognition through the introduction of a latent-level enhancement technique. The innovative FM-Refiner effectively bridges the gap between noisy and clean representations, demonstrating substantial improvements in ASR performance across various noise conditions.
Methodology
The paper introduces a novel approach to automatic speech recognition (ASR) by proposing the Flow Matching Refinement module (FM-Refiner), which operates on the latent representations of a pretrained CTC-based ASR encoder. The methodology is well-structured, leveraging flow matching to refine distorted latents into cleaner representations. The plug-and-play nature of the FM-Refiner allows it to be integrated into existing ASR systems without the need for retraining, which is a significant advantage. The use of flow matching as a deterministic transport mapping is innovative and addresses the common issue of residual distortions in traditional speech enhancement methods.
Results & evaluation
The experimental setup is robust, utilizing multiple datasets and noise conditions to evaluate the effectiveness of the FM-Refiner. The results consistently demonstrate a reduction in word error rate (WER) across various scenarios, both with and without prior speech enhancement. This thorough evaluation provides strong empirical support for the proposed method's effectiveness, showcasing its applicability across different SE front-ends.
Reproducibility
The paper provides sufficient details regarding the architecture of the FM-Refiner and the ASR model, including training parameters and dataset descriptions. However, the lack of a publicly available code repository limits the reproducibility of the results. Including a link to the implementation would greatly enhance the ability for others to replicate the findings.
Limitations
One limitation of the study is the reliance on pretrained models, which may not generalize well to all ASR tasks or noise conditions. Additionally, while the results are promising, the paper does not explore the computational efficiency of the FM-Refiner during inference, which could be a concern for real-time applications.
Broader impact
The proposed method has significant implications for improving ASR systems in real-world noisy environments, which is crucial for applications in telecommunications, voice-activated systems, and accessibility technologies. By enhancing the robustness of ASR, this research could lead to better user experiences and broader adoption of voice recognition technologies. This paper presents a significant advancement in noise-robust automatic speech recognition through the introduction of a latent-level enhancement technique. The innovative FM-Refiner effectively bridges the gap between noisy and clean representations, demonstrating substantial improvements in ASR performance across various noise conditions.
Author of this paper? Send a correction.