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We introduce a new technique to improve the recognition of non-native speech. The underlying assumption is that for each non-native pronunciation of a speech sound, there is at least one sound in the target language that has a similar native pronunciation. The adaptation is performed by HMM interpolation between adequate native acoustic models. The interpolation partners are determined automatically in a data-driven manner. Our experiments show that this technique is suitable for both the off-line adaptation to a whole group of speakers as well as for the unsupervised online adaptation to a single speaker. Results are given both for spontaneous non-native English speech as well as for a set of read non-native German utterances.