Explanation of the demo
Acoustic models trained on a certain type of data work really well on similar samples, however, as soon as they encounter different recording conditions, they are lost. Adapting the model allows basically recycling it to work on these new conditions instead of simply throwing it away. Moreover, this approach generally gives better results than a model trained from scratch, because the model will have a meaningful initialization instead of a random one.
In our case, we also use a Domain Adversarial Network to force the adapted model to generalize on the two domains so that it can work as well on one as on the other.