24. The at least one machine readable memory of claim 17 , wherein the plurality of decision rules is a first plurality of decision rules and the selected decision rule is a first decision rule that partitions the first set of training samples into at least second and third sets of training samples, and wherein the at least one processor is further configured to construct the decision tree at least in part by: applying a second plurality of decision rules to the second set of training samples, each of the second plurality of decision rules partitioning the second set of training samples into at least two subsets of the second set of training samples; for each one of the second plurality of decision rules, computing a corresponding measure of impurity indicative of an extent to which each of the at least two subsets formed by applying the one of the second plurality of decision rules contains training samples associated with different pronunciation labels; and selecting, for the second node of the decision tree, a second decision rule from the second plurality of decision rules based at least in part on the measures of impurity computed for the second plurality of decision rules.
17. At least one machine readable memory, having stored thereon a computer program having a plurality of code sections executable by at least one machine for causing the at least one machine to perform a computer-implemented method for constructing a test for use in disambiguating a homograph during a computer-based text-to-speech event, the method comprising steps of: using at least one processor to construct a decision tree for determining a pronunciation label for the homograph in an input word string, the decision tree comprising at least first and second nodes, the first node being a parent of the second node, wherein the at least one processor is configured to construct the decision tree at least in part by: accessing a first set of training samples, each of the training samples comprising a word string that contains the homograph and a pronunciation label indicating a correct pronunciation of the homograph in the word string; applying a plurality of decision rules to the first set of training samples, each of the plurality of decision rules partitioning the first set of training samples into at least two subsets of the first set of training samples; for each one of the plurality of decision rules, computing a corresponding measure of impurity indicative of an extent to which each of the at least two subsets formed by applying the one of the plurality of decision rules contains training samples associated with different pronunciation labels, wherein the one of the plurality of decision rules, when applied to word strings in the first set of training samples, determines whether at least one selected word indicator is present in the word strings, and wherein at least one training sample in the first set of training samples is retained for computing the measure of impurity corresponding to the one of the plurality of decision rules even if the at least one selected word indicator is absent in the word string of the at least one training sample; and selecting, for the first node of the decision tree, a decision rule from the plurality of decision rules based at least in part on the measures of impurity computed for the plurality of decision rules.