This paper deals with the problem of non-native accents in speech recognition. Reference tests were performed using whole-word and sub-word models trained either with a native accent or a pool of native and non-native accents. The results seem to indicate that the use of phonetic transcriptions for each specific accent may improve recognition scores with sub-word models. A data-driven process is used to derive transcription lattices. The recognition scores thus obtained were encouraging.