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AUTOMATIC LEARNING OF FINITE STATE AUTOMATA FOR PRONUNCIATION MODELING.. Proc. of the EuroSpeech, 2001.The great variability of word pronunciations in spontaneous speech is one of the reasons for the low performance of the present speech recognition systems. The generation of dictionaries that take into account this variability can increase the robustness of such systems. A word pronunciation is a possible phone sequence that can appear in a real utterance, and represents a possible acoustic realization of the word. In this paper, word pronunciations are modeled using finite state automata. The use of such models allow for the application of grammatical inference methods and an easy integration with the others sources of acknowledge. The training samples are obtained from the alignment between the phone decodification of each training utterance and the corresponding canonical transcription. Models proposed in this work were applied in a translation-oriented speech task. The improvements achieved by these new models were in the range between 2.7 to 0.6 points depending on the language model used.