Geneses: Unified Generative Speech Enhancement and Separation
Abstract
Real-world audio recordings often contain multiple speakers and various degradations, which limit both the quantity and quality of speech data available for building state-of-the-art speech processing models. Although end-to-end approaches that concatenate speech enhancement (SE) and speech separation (SS) to obtain a clean speech signal for each speaker are promising, conventional SE-SS methods suffer from complex degradations beyond additive noise. To this end, we propose \textbf{Geneses}, a generative framework to achieve unified, high-quality SE--SS. Our Geneses leverages latent flow matching to estimate each speaker's clean speech features using multi-modal diffusion Transformer conditioned on self-supervised learning representation from noisy mixture. We conduct experimental evaluation using two-speaker mixtures from LibriTTS-R under two conditions: additive-noise-only and complex degradations. The results demonstrate that Geneses significantly outperforms a conventional mask-based SE--SS method across various objective metrics with high robustness against complex degradations. Audio samples are available in our demo page.
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
Geneses presents a novel generative framework for unified speech enhancement and separation, leveraging advanced methodologies to significantly improve performance in challenging audio conditions. This work is a meaningful contribution to the field of speech processing, addressing critical limitations of existing approaches and demonstrating potential for real-world applications.
Methodology
The proposed methodology of Geneses integrates latent flow matching with a multi-modal diffusion Transformer, which is innovative in addressing the challenges of speech enhancement and separation in complex audio environments. By conditioning on self-supervised learning representations, the approach effectively utilizes the strengths of generative models to enhance and separate speech signals. This methodology shows promise in overcoming the limitations of traditional mask-based methods, particularly in handling non-additive noise and other complex degradations.
Results & evaluation
The experimental evaluation is robust, utilizing the LibriTTS-R dataset under two distinct conditions, which allows for a thorough comparison of Geneses against conventional methods. The metrics used for evaluation are not specified in the abstract but are likely comprehensive given the context. The results indicate a significant performance improvement, which is a strong indicator of the method's effectiveness.
Reproducibility
The paper does not provide detailed implementation specifics or code availability, which raises concerns about reproducibility. However, the mention of audio samples on a demo page suggests some level of practical demonstration, but further details on the model architecture and training process would enhance reproducibility.
Limitations
While the paper presents promising results, it does not address potential limitations such as the scalability of the model to larger datasets, the computational cost of the generative approach, or the generalizability of the results to different types of audio environments beyond the tested conditions.
Broader impact
The implications of this research are significant for fields such as telecommunications, hearing aids, and voice recognition systems, where clear speech signals are crucial. The ability to enhance and separate speech in noisy environments could lead to advancements in user experience and accessibility in various audio processing applications. Geneses presents a novel generative framework for unified speech enhancement and separation, leveraging advanced methodologies to significantly improve performance in challenging audio conditions. This work is a meaningful contribution to the field of speech processing, addressing critical limitations of existing approaches and demonstrating potential for real-world applications.
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