VoiceBridge: Designing Latent Bridge Models for General Speech Restoration at Scale
Abstract
Bridge models have recently been explored for speech enhancement tasks such as denoising, dereverberation, and super-resolution, while these efforts are typically confined to a single task or small-scale datasets, with constrained general speech restoration (GSR) capability at scale. In this work, we introduce VoiceBridge, a GSR system rooted in latent bridge models (LBMs), capable of reconstructing high-fidelity speech at full-band (\textit{i.e.,} 48~kHz) from various distortions. By compressing speech waveform into continuous latent representations, VoiceBridge models the~\textit{diverse LQ-to-HQ tasks} (namely, low-quality to high-quality) in GSR with~\textit{a single latent-to-latent generative process} backed by a scalable transformer architecture. To better inherit the advantages of bridge models from the data domain to the latent space, we present an energy-preserving variational autoencoder, enhancing the alignment between the waveform and latent space over varying energy levels. Furthermore, to address the difficulty of HQ reconstruction from distinctively different LQ priors, we propose a joint neural prior, uniformly alleviating the reconstruction burden of LBM. At last, considering the key requirement of GSR systems, human perceptual quality, a perceptually aware fine-tuning stage is designed to mitigate the cascading mismatch in generation while improving perceptual alignment. Extensive validation across in-domain and out-of-domain tasks and datasets (\textit{e.g.}, refining recent zero-shot speech and podcast generation results) demonstrates the superior performance of VoiceBridge. Demo samples can be visited at: https://VoiceBridge-demo.github.io/.
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
VoiceBridge introduces a novel framework for general speech restoration using latent bridge models, achieving high-fidelity audio reconstruction across diverse degradation scenarios. The paper's contributions are substantial, providing a robust methodology and comprehensive experimental validation that positions it as a significant advancement in the field of audio processing and machine learning.
Methodology
The methodology presented in VoiceBridge is innovative, leveraging latent bridge models (LBMs) to tackle the general speech restoration (GSR) problem at scale. The paper introduces several key innovations, including an energy-preserving variational autoencoder (EP-VAE) that enhances the alignment between waveform and latent space, a joint neural prior that reduces the reconstruction burden, and a perceptually aware fine-tuning stage that aligns the model's outputs with human perceptual quality. The use of a transformer architecture for the generative process is particularly noteworthy, as it allows for scalable modeling of diverse low-quality to high-quality speech tasks. The integration of perceptual metrics into the training process is a significant advancement in ensuring that the generated audio is not only technically accurate but also subjectively pleasing.
Results & evaluation
The experiments conducted are extensive, covering both in-domain and out-of-domain tasks. The authors validate their approach on multiple datasets, demonstrating superior performance across various metrics, including perceptual quality assessments. The results indicate that VoiceBridge consistently outperforms existing models, showcasing its robustness in handling different types of speech degradation. The inclusion of user studies to assess human preference further strengthens the evaluation, providing a comprehensive view of the model's effectiveness.
Reproducibility
The paper provides detailed information about the model architecture, training pipeline, and datasets used, which is crucial for reproducibility. However, the absence of a publicly accessible code repository limits the ability of other researchers to replicate the results fully. While the authors mention the use of specific architectures and training strategies, the lack of open-source code may hinder broader validation and application of the findings.
Limitations
One limitation of the study is the reliance on specific datasets for training and evaluation, which may not encompass all real-world scenarios. The model's performance in extreme degradation conditions, while promising, has not been exhaustively tested across all potential use cases. Additionally, the computational demands of training such a large model may limit accessibility for researchers with fewer resources.
Broader impact
The advancements made in VoiceBridge have significant implications for various applications, including telecommunications, assistive technologies for hearing-impaired individuals, and content creation in media. By improving the quality of speech restoration, the model can enhance communication in noisy environments and contribute to more effective speech synthesis systems. The focus on perceptual quality also aligns with growing interest in user-centric AI applications, making this work relevant to both academic and industry stakeholders. VoiceBridge introduces a novel framework for general speech restoration using latent bridge models, achieving high-fidelity audio reconstruction across diverse degradation scenarios. The paper's contributions are substantial, providing a robust methodology and comprehensive experimental validation that positions it as a significant advancement in the field of audio processing and machine learning.
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