  On-device Automatic Speech Recognition (ASR) models trained on speech data of
a large population might underperform for individuals unseen during training.
This is due to a domain shift between user data and the original training data,
differed by user's speaking characteristics and environmental acoustic
conditions. ASR personalisation is a solution that aims to exploit user data to
improve model robustness. The majority of ASR personalisation methods assume
labelled user data for supervision. Personalisation without any labelled data
is challenging due to limited data size and poor quality of recorded audio
samples. This work addresses unsupervised personalisation by developing a novel
consistency based training method via pseudo-labelling. Our method achieves a
relative Word Error Rate Reduction (WERR) of 17.3% on unlabelled training data
and 8.1% on held-out data compared to a pre-trained model, and outperforms the
current state-of-the art methods.
