3 accepted papers
This paper proposes a generative pretraining foundation model for high-quality speech restoration tasks. By directly operating on complex-valued short-time Fourier transform coefficients, our model does not rely on any vocoders for time-domain signal reconstruction. As a result, our model simplifies…
Diffusion-based generative models have been shown to be highly effective in various speech enhancement tasks. This work presents an analysis of a flow matching-based framework for generative speech enhancement as a simpler alternative to diffusion. Four different modifications to flow matching are p…
In this work, we investigate application of generative speech enhancement to improve the robustness of ASR models in noisy and reverberant conditions. We employ a recently-proposed speech enhancement model based on Schrödinger bridge, which has been shown to perform well compared to diffusion-based…