A Minimum Classification Error Approach To Pronunciation Variation Modeling Of Non-Native Proper Names

In automatic recognition of non-native proper names, it is critical to be able to handle a variety of different pronunciations. Traditionally, this has been solved by including alternative pronunciation variants in the recognition lexicon at the risk of introducing unwanted confusion between different name entries. In this paper we propose a pronunciation variant selection criterion that aims to avoid this risk by basing its decisions on scores which are calculated according to the minimum classification error (MCE) framework. By comparing the error rate before and after a lexicon change, the selection criterion chooses only the candidates that actually decrease the error rate. Selecting pronunciation candidates in this manner substantially reduces both the error rate and the required number of variants per name compared to a probability-based baseline selection method.