  Diffusion models are a new class of generative models that have recently been
applied to speech enhancement successfully. Previous works have demonstrated
their superior performance in mismatched conditions compared to state-of-the
art discriminative models. However, this was investigated with a single
database for training and another one for testing, which makes the results
highly dependent on the particular databases. Moreover, recent developments
from the image generation literature remain largely unexplored for speech
enhancement. These include several design aspects of diffusion models, such as
the noise schedule or the reverse sampler. In this work, we systematically
assess the generalization performance of a diffusion-based speech enhancement
model by using multiple speech, noise and binaural room impulse response (BRIR)
databases to simulate mismatched acoustic conditions. We also experiment with a
noise schedule and a sampler that have not been applied to speech enhancement
before. We show that the proposed system substantially benefits from using
multiple databases for training, and achieves superior performance compared to
state-of-the-art discriminative models in both matched and mismatched
conditions. We also show that a Heun-based sampler achieves superior
performance at a smaller computational cost compared to a sampler commonly used
for speech enhancement.
