  The performance of automatic speech recognition systems can be improved by
adapting an acoustic model to compensate for the mismatch between training and
testing conditions, for example by adapting to unseen speakers. The success of
speaker adaptation methods relies on selecting weights that are suitable for
adaptation and using good adaptation schedules to update these weights in order
not to overfit to the adaptation data. In this paper we investigate a
principled way of adapting all the weights of the acoustic model using a
meta-learning. We show that the meta-learner can learn to perform supervised
and unsupervised speaker adaptation and that it outperforms a strong baseline
adapting LHUC parameters when adapting a DNN AM with 1.5M parameters. We also
report initial experiments on adapting TDNN AMs, where the meta-learner
achieves comparable performance with LHUC.
