Lorenzo Vassallo

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In this paper, we report on the performance of two variants of well-known statistical-based clustering techniques and present an evaluation on the TIMIT and TI-Digit databases. A clustering approach which 1) is based on a divergence criterion, 2) separates " good " and " bad " models using a class-dependent adjustable threshold on the number of examples per(More)
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