Investigating Training Objectives for Generative Speech Enhancement

Julius Richter; Danilo de Oliveira; Timo Gerkmann

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arXiv · 2409.10753

Investigating Training Objectives for Generative Speech Enhancement

Julius Richter·Danilo de Oliveira·Timo Gerkmann

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Abstract

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 to explain the differences between these frameworks by focusing our investigation on score-based generative models and the 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 domain.

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Julius Richter, Danilo de Oliveira, Timo Gerkmann. 2024-09-16. Investigating Training Objectives for Generative Speech Enhancement. https://arxiv.org/abs/2409.10753

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