In this paper we introduce a speaker verification system deployed on mobile devices that can be used to personalise a keyword spotter. We describe a baseline DNN system that maps an utterance to a speaker embedding, which is used to measure speaker differences via cosine similarity. We then introduce an architectural modification which uses an LSTM system where the parameters are optimised via a curriculum learning procedure to reduce the detection error and improve its generalisability across various conditions. Experiments on our internal datasets show that the proposed approach outperforms the DNN baseline system and yields a relative EER reduction of 30–70% on both text-dependent and text-independent tasks under a variety of acoustic conditions.