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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.