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In this article, we present an approach for non native automatic speechrecognition (ASR). We propose two methods to adapt existing ASR systems to thenon-native accents. The first method is based on the modification of acousticmodels through integration of acoustic models from the mother tong. Thephonemes of the target language are pronounced in a similar manner to thenative language of speakers. We propose to combine the models of confusedphonemes so that the ASR system could recognize both concurrentpronounciations. The second method we propose is a refinment of thepronounciation error detection through the introduction of graphemicconstraints. Indeed, non native speakers may rely on the writing of words intheir uttering. Thus, the pronounctiation errors might depend on the characterscomposing the words. The average error rate reduction that we observed is(22.5%) relative for the sentence error rate, and 34.5% (relative) in worderror rate.

Am\'elioration des Performances des Syst\`emes Automatiques de Reconnaissance de la Parole pour la Parole Non Native