  In Automatic Speech Recognition (ASR), teacher-student (T/S) training has
shown to perform well for domain adaptation with small amount of training data.
However, adaption without ground-truth labels is still challenging. A previous
study has shown the effectiveness of using ensemble teacher models in T/S
training for unsupervised domain adaptation (UDA) but its performance still
lags behind compared to the model trained on in-domain data. This paper
proposes a method to yield better UDA by training multi-stage students with
ensemble teacher models. Initially, multiple teacher models are trained on
labelled data from read and meeting domains. These teachers are used to train a
student model on unlabelled out-of-domain telephone speech data. To improve the
adaptation, subsequent student models are trained sequentially considering
previously trained model as their teacher. Experiments are conducted with three
teachers trained on AMI, WSJ and LibriSpeech and three stages of students on
SwitchBoard data. Results shown on eval00 test set show significant WER
improvement with multi-stage training with an absolute gain of 9.8%, 7.7% and
3.3% at each stage.
