Lightweight Wasserstein Audio-Visual Model for Unified Speech Enhancement and Separation
Abstract
Speech Enhancement (SE) and Speech Separation (SS) have traditionally been treated as distinct tasks in speech processing. However, real-world audio often involves both background noise and overlapping speakers, motivating the need for a unified solution. While recent approaches have attempted to integrate SE and SS within multi-stage architectures, these approaches typically involve complex, parameter-heavy models and rely on supervised training, limiting scalability and generalization. In this work, we propose UniVoiceLite, a lightweight and unsupervised audio-visual framework that unifies SE and SS within a single model. UniVoiceLite leverages lip motion and facial identity cues to guide speech extraction and employs Wasserstein distance regularization to stabilize the latent space without requiring paired noisy-clean data. Experimental results demonstrate that UniVoiceLite achieves strong performance in both noisy and multi-speaker scenarios, combining efficiency with robust generalization. The source code is available at https://github.com/jisoo-o/UniVoiceLite.
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 introduction of UniVoiceLite, a lightweight and unsupervised audio-visual framework that unifies speech enhancement and separation. This work represents a significant advancement in the field of speech processing by addressing the challenges of scalability and generalization in existing models, while also providing a novel approach that leverages both audio and visual information.
Methodology
The proposed methodology of UniVoiceLite is innovative in its integration of audio-visual cues (lip motion and facial identity) for speech enhancement and separation, which is a significant departure from traditional methods that treat these tasks separately. The use of Wasserstein distance regularization to stabilize the latent space without needing paired noisy-clean data is particularly noteworthy, as it addresses a common limitation in supervised learning approaches. The lightweight design of the model also suggests a focus on efficiency, which is critical for real-world applications.
Results & evaluation
The experimental results presented in the paper indicate strong performance in both noisy and multi-speaker scenarios, demonstrating the model's robustness and generalization capabilities. However, the paper would benefit from a more detailed description of the datasets used, including their size and diversity, as well as comparisons with baseline models to better contextualize the improvements achieved by UniVoiceLite.
Reproducibility
The availability of the source code on GitHub is a positive aspect that enhances reproducibility. However, the paper should provide more comprehensive implementation details, including hyperparameter settings and training procedures, to facilitate easier replication of results by other researchers.
Limitations
One limitation of the proposed model is its reliance on visual cues, which may not always be available in all real-world scenarios. Additionally, while the model is designed to be lightweight, the trade-off between model complexity and performance should be further explored, particularly in edge cases or under extreme noise conditions.
Broader impact
The potential applications of this research extend to various fields such as telecommunications, assistive technologies for the hearing impaired, and multimedia processing. By improving speech clarity in noisy environments, this work could significantly enhance communication effectiveness in diverse settings. The main contribution of this paper is the introduction of UniVoiceLite, a lightweight and unsupervised audio-visual framework that unifies speech enhancement and separation. This work represents a significant advancement in the field of speech processing by addressing the challenges of scalability and generalization in existing models, while also providing a novel approach that leverages both audio and visual information.
Author of this paper? Send a correction.