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In this section the problem of recognising non-native speech was examined and a new method for generating non-native pronunciations without using non-native data was proposed. It was shown that performance decreases if a recogniser that is optimised for native speech is exposed to non-native speech. Investigations on the ISLE database showed that including specialised non- native pronunciation variants in the dictionary can greatly improve the results. A set of rules was manually derived and the optimal set of rules was determined for each speaker in the test set. The application of these specialised rules was clearly superior to using all rules or rules that are typical for the native language of a speaker group. However,since the manual derivation is not feasible if the combination of several source and target languages is to be covered,a new method was introduced that solely relies on native speech to derive non-native variants automatically. The big advantage of the proposed method is that it requires native data only,and can thus be repeated for all the languages for which HMM models trained on native speech are available.
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