Investigating Training Objectives for Generative Speech Enhancement
<|reference_start|>Investigating Training Objectives for Generative Speech Enhancement: Generative speech enhancement has recently shown promising advancements in improving speech quality in noisy environments. Multiple diffusion-based frameworks exist, each employing distinct training objectives and learning techniques. This paper aims at explaining the differences between these frameworks by focusing our investigation on score-based generative models and Schr\"odinger bridge. We conduct a series of comprehensive experiments to compare their performance and highlight differing training behaviors. Furthermore, we propose a novel perceptual loss function tailored for the Schr\"odinger bridge framework, demonstrating enhanced performance and improved perceptual quality of the enhanced speech signals. All experimental code and pre-trained models are publicly available to facilitate further research and development in this.<|reference_end|>
@article{richter2024investigating,
    title={Investigating Training Objectives for Generative Speech Enhancement},
    author={Julius Richter, Danilo de Oliveira, Timo Gerkmann},
    journal={arXiv preprint arXiv:2409.10753},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10753},
    primaryClass={eess.AS cs.SD}
}