Universal Score-based Speech Enhancement with High Content Preservation
<|reference_start|>Universal Score-based Speech Enhancement with High Content Preservation: We propose UNIVERSE++, a universal speech enhancement method based on score-based diffusion and adversarial training. Specifically, we improve the existing UNIVERSE model that decouples clean speech feature extraction and diffusion. Our contributions are three-fold. First, we make several modifications to the network architecture, improving training stability and final performance. Second, we introduce an adversarial loss to promote learning high quality speech features. Third, we propose a low-rank adaptation scheme with a phoneme fidelity loss to improve content preservation in the enhanced speech. In the experiments, we train a universal enhancement model on a large scale dataset of speech degraded by noise, reverberation, and various distortions. The results on multiple public benchmark datasets demonstrate that UNIVERSE++ compares favorably to both discriminative and generative baselines for a wide range of qualitative and intelligibility metrics.<|reference_end|>
@article{scheibler2024universal,
    title={Universal Score-based Speech Enhancement with High Content Preservation},
    author={Robin Scheibler, Yusuke Fujita, Yuma Shirahata, Tatsuya Komatsu},
    journal={arXiv preprint arXiv:2406.12194},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12194},
    primaryClass={eess.AS cs.SD}
}