Pronunciation variation modeling of non-native proper names by discriminative tree search

In this paper, the task of selecting the optimal subset of pronunciation variants from a set of automatically generated candidates is recast as a tree search problem. In this approach, the optimal recognition lexicon corresponds with the optimal path through a search tree. We define a discriminative evaluation function to guide the search algorithm, which is based on estimates of the number of recognition errors before and after a lexicon change. The error rate for a given lexicon is estimated using the Minimum Classification Error framework. Selecting pronunciation candidates by means of this search algorithm clearly outperforms a baseline selection method, resulting in a reduction of both the error rate and the required number of variants in the recognition lexicon.