Diffusion-based speech enhancement with a weighted generative-supervised
  learning loss
Jean-Eudes Ayilo Mostafa Sadeghi Romain Serizel
Diffusion-based generative models have recently gained attention in speech
enhancement (SE), providing an alternative to conventional supervised methods.
These models transform clean speech training samples into Gaussian noise
centered at noisy speech, and subsequently learn a parameterized model to
reverse this process, conditionally on noisy speech. Unlike supervised methods,
generative-based SE approaches usually rely solely on an unsupervised loss,
which may result in less efficient incorporation of conditioned noisy speech.
To address this issue, we propose augmenting the original diffusion training
objective with a mean squared error (MSE) loss, measuring the discrepancy
between estimated enhanced speech and ground-truth clean speech at each reverse
process iteration. Experimental results demonstrate the effectiveness of our
proposed methodology.