Questions about MetricGAN

[NAM-hj]

While I study MetricGAN, I have some questions.

1. What is a generative component of MetricGAN?
   For example, SEGAN[1] has a generative component. It concatenates "random latent vectors" to its encoder output.

2. Do you classify this work as a generative or discriminative method?

3. Have you tried a pre-trained discriminator(D) that is trained with training data and then fixed while training the generator(G)?
   As you mentioned in the paper, D should be called an evaluator. The training procedure of MetricGAN is not adversarial. G and D help each other.
   Therefore, I'm wondering which is better, 'training G with pre-trained D' or 'training G and D together'.

[1] Pascual, Santiago, Antonio Bonafonte, and Joan Serra. "SEGAN: Speech enhancement generative adversarial network." arXiv preprint arXiv:1703.09452 (2017).

Thank you very much for sharing your great work.