They first train an NNJM using in-domain data, and then perform regularized adaptation on the general domain data (concatenation of in-domain and out-of-domain data) which restricts the model from drifting away from the in-domain NNJM.
they add a regularization term J to the objective function in their adaptive training step: where p ID (y|h) id probability distribution estimated by the in-domain NNJM.