A COMPARATIVE STUDY OF METHODS FOR PHONETIC DECISION-TREE STATE CLUSTERING
H.J. Nock, M.J.F. Gales and S.J. Young
September 1997
Phonetic decision trees have been widely used for obtaining robust context-dependent models in HMM-based systems. There are five key issues to consider when constructing phonetic decision trees: the alignment of data with the chosen phone classes; the quality of the modelling of the underlying data; the choice of partitioning method at each node; the goodness-of-split criterion and the method for determining appropriate tree sizes. A popular existing method uses efficient but crude approximate methods for each of these. This paper introduces and evaluates more detailed alternatives to the standard approximations.
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