Dynamic generation of proper name pronunciations for directory assistance

This paper deals with the difficult task of recognition of a large vocabulary of proper names in a directory assistance application. Rather than augmenting the lexicon with alternate pronunciations. which is unsuitable for very large vocabularies of proper names, a class of distortions of the canonical form is used as knowledge source (KS) for a new evaluation of the N best hypotheses generated in a first recognition phase, in which new probability distributions are used. The KS is the result of applying constraints inspired by speech science to distortions obtained by automatic learning. Experiments on a very large French directory document the validity of the approach.