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In this paper, we investigate the application of maximum entropy discrimination (MED) feature selection in speech recognition problems. We compare the MED algorithm with a classical wrapper feature selection algorithm and we propose a hybrid wrapper/MED algorithm. We experiment with the three approaches on a phoneme recognition task on the TIMIT database. Results show that the MED algorithm achieves error rates comparable with the wrapper algorithm, requiring a reduced computational charge. Furthermore, the use of a probabilistic framework shows that the MED algorithm gives very good results even with a very limited amount of data.
Date of Conference: 30 Nov.-3 Dec. 2003