Rescoring multiple pronunciations generated from spelled words

Building on earlier work [2], we show how a set of binary decision trees grown by means of the Gelfand-Ravishankar-Delp algorithm [8] can be trained to generate an ordered list of possible pronunciations from a spelled word. Training is carried out on a database consisting of spelled words paired with their pronunciations (in a particular language). We show how phonotactic information can be learned by a second set of decision trees, which reorder the multiple pronunciations generated by the first set. The paper defines the “inclusion” metric for scoring phoneticizers that generate multiple pronunciations. Experimental results employing this metric indicate that phonotactic reordering yields a slight improvement when only the top pronunciation is retained, and a large improvement when more than one hypothesis is retained. Isolated-word recognition results which show good performance for automatically-generated pronunciations are given.

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