  We propose a method for zero-resource domain adaptation of DNN acoustic
models, for use in low-resource situations where the only in-language training
data available may be poorly matched to the intended target domain. Our method
uses a multi-lingual model in which several DNN layers are shared between
languages. This architecture enables domain adaptation transforms learned for
one well-resourced language to be applied to an entirely different low-resource
language. First, to develop the technique we use English as a well-resourced
language and take Spanish to mimic a low-resource language. Experiments in
domain adaptation between the conversational telephone speech (CTS) domain and
broadcast news (BN) domain demonstrate a 29% relative WER improvement on
Spanish BN test data by using only English adaptation data. Second, we
demonstrate the effectiveness of the method for low-resource languages with a
poor match to the well-resourced language. Even in this scenario, the proposed
method achieves relative WER improvements of 18-27% by using solely English
data for domain adaptation. Compared to other related approaches based on
multi-task and multi-condition training, the proposed method is able to better
exploit well-resource language data for improved acoustic modelling of the
low-resource target domain.
