A Teacher-student Framework for Unsupervised Speech Enhancement Using Noise Remixing Training and Two-stage Inference. (arXiv:2210.15368v2 [cs.SD] UPDATED)

The lack of clean speech is a practical challenge to the development of
speech enhancement systems, which means that the training of neural network
models must be done in an unsupervised manner, and there is an inevitable
mismatch between their training criterion and evaluation metric. In response to
this unfavorable situation, we propose a teacher-student training strategy that
does not require any subjective/objective speech quality metrics as learning
reference by improving the previously proposed noisy-target training (NyTT).
Because homogeneity between …

arxiv framework inference noise speech stage training unsupervised