To evaluate transfer learning performance, we consider three criteria: (1) inclusion of phonetic content, (2) exclusion of nuisance factors, and (3) transferrability across datasets. The first two are evaluated using a protocol similar to <cite>[17]</cite> , where an ASR model is trained on a set of domains, and evaluated on both in-domain and out-of-domain speech (relative to the training data).