Towards Real-Time Generative Speech Restoration with Flow-Matching
Abstract
Generative models have shown robust performance on speech enhancement and restoration tasks, but most prior approaches operate offline with high latency, making them unsuitable for streaming applications. In this work, we investigate the feasibility of a low-latency, real-time generative speech restoration system based on flow-matching (FM). Our method tackles diverse real-world tasks, including denoising, dereverberation, and generative restoration. The proposed causal architecture without time-downsampling achieves introduces an total latency of only 20 ms, suitable for real-time communication. In addition, we explore a broad set of architectural variations and sampling strategies to ensure effective training and efficient inference. Notably, our flow-matching model maintains high enhancement quality with only 5 number of function evaluations (NFEs) during sampling, achieving similar performance as when using ~20 NFEs under the same conditions. Experimental results indicate that causal FM-based models favor few-step reverse sampling, and smaller backbones degrade with longer reverse trajectories. We further show a side-by-side comparison of FM to typical adversarial-loss-based training for the same model architecture.
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
The main contribution of this paper is the development of a low-latency, real-time generative speech restoration system using flow-matching. The work is significant as it addresses the critical challenge of latency in speech enhancement, providing a promising direction for future research in real-time audio processing.
Methodology
The paper introduces a novel causal architecture for flow-matching in speech restoration, which is significant for real-time applications. The methodology is well-structured, leveraging flow-matching principles to achieve low-latency processing. The authors provide a comprehensive explanation of their approach, including the mathematical foundation and architectural choices, which are critical for understanding the model's performance. However, the paper could benefit from a more detailed discussion of the architectural variations explored and their specific impacts on performance.
Results & evaluation
The experimental setup is robust, utilizing established datasets (DNS and SIG challenges) for evaluation. The authors provide a thorough analysis of their model's performance across different metrics, including SIGMOS and DistillMOS, which are relevant for assessing audio quality. The comparison with GAN-based models is particularly insightful, highlighting the strengths and weaknesses of flow-matching in real-time scenarios. However, the results could be enhanced with more extensive ablation studies to isolate the effects of various architectural choices.
Reproducibility
The paper outlines the training and evaluation processes clearly, including data augmentation strategies and evaluation metrics. However, the lack of a publicly available code repository limits reproducibility. Providing access to the model and training scripts would significantly enhance the ability of other researchers to replicate the findings.
Limitations
The paper acknowledges the limitations of the flow-matching approach, particularly in comparison to GAN-based methods. It notes that while the proposed models achieve low latency, they do not consistently outperform existing techniques, indicating room for improvement. Additionally, the performance degradation observed with smaller backbones and longer reverse trajectories suggests that further optimization is needed.
Broader impact
This research has significant implications for real-time communication applications, such as VoIP and teleconferencing, where low-latency speech restoration is crucial. The findings could influence future work in generative models for audio processing, potentially leading to advancements in other areas of speech technology. The main contribution of this paper is the development of a low-latency, real-time generative speech restoration system using flow-matching. The work is significant as it addresses the critical challenge of latency in speech enhancement, providing a promising direction for future research in real-time audio processing.
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