SLD-L2S: Hierarchical Subspace Latent Diffusion for High-Fidelity Lip to Speech Synthesis
Abstract
Although lip-to-speech synthesis (L2S) has achieved significant progress in recent years, current state-of-the-art methods typically rely on intermediate representations such as mel-spectrograms or discrete self-supervised learning (SSL) tokens. The potential of latent diffusion models (LDMs) in this task remains largely unexplored. In this paper, we introduce SLD-L2S, a novel L2S framework built upon a hierarchical subspace latent diffusion model. Our method aims to directly map visual lip movements to the continuous latent space of a pre-trained neural audio codec, thereby avoiding the information loss inherent in traditional intermediate representations. The core of our method is a hierarchical architecture that processes visual representations through multiple parallel subspaces, initiated by a subspace decomposition module. To efficiently enhance interactions within and between these subspaces, we design the diffusion convolution block (DiCB) as our network backbone. Furthermore, we employ a reparameterized flow matching technique to directly generate the target latent vectors. This enables a principled inclusion of speech language model (SLM) and semantic losses during training, moving beyond conventional flow matching objectives and improving synthesized speech quality. Our experiments show that SLD-L2S achieves state-of-the-art generation quality on multiple benchmark datasets, surpassing existing methods in both objective and subjective evaluations.
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 paper presents SLD-L2S, a novel framework for high-fidelity lip-to-speech synthesis that leverages a hierarchical subspace latent diffusion model, achieving state-of-the-art results in synthesis quality. The methodology is innovative and addresses critical challenges in the field, while the experimental evaluation supports its effectiveness, though the lack of a publicly available implementation may hinder reproducibility.
Methodology
The paper introduces a novel framework, SLD-L2S, which employs a hierarchical subspace latent diffusion model to directly map visual lip movements to the latent space of a pre-trained audio codec. The methodology is innovative in its use of diffusion convolution blocks (DiCB) and a reparameterized flow matching technique, which enhances the model's ability to generate high-fidelity speech without relying on traditional intermediate representations like mel-spectrograms. The hierarchical architecture and subspace decomposition approach are well-justified, addressing the inherent challenges of lip-to-speech synthesis effectively.
Results & evaluation
The experiments are robust, utilizing multiple benchmark datasets (LRS3-TED and LRS2-BBC) to validate the performance of the proposed method. The results demonstrate that SLD-L2S achieves state-of-the-art performance in both objective and subjective evaluations, significantly outperforming existing methods. The use of comprehensive metrics, including UTMOS, SCOREQ, WER, and subjective MOS tests, provides a well-rounded assessment of the model's capabilities.
Reproducibility
The paper provides detailed implementation details, including architecture configurations, training procedures, and hyperparameter settings, which are essential for reproducibility. However, the absence of a publicly available code repository or demo URL limits the practical reproducibility of the results.
Limitations
One notable limitation is the lack of a clear discussion on the potential computational costs associated with the proposed method, particularly in real-world applications. Additionally, the paper does not address the scalability of the model to different languages or accents, which could impact its generalizability.
Broader impact
The proposed SLD-L2S framework has significant implications for various applications, including automated video dubbing, assistive technologies for individuals with speech impairments, and enhancing communication in noisy environments. By improving the quality and intelligibility of synthesized speech from visual inputs, this work could facilitate more natural interactions in human-computer interfaces. The paper presents SLD-L2S, a novel framework for high-fidelity lip-to-speech synthesis that leverages a hierarchical subspace latent diffusion model, achieving state-of-the-art results in synthesis quality. The methodology is innovative and addresses critical challenges in the field, while the experimental evaluation supports its effectiveness, though the lack of a publicly available implementation may hinder reproducibility.
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