Automated generation of N-best pronunciations of proper nouns

The problem of proper noun recognition is key to developing pervasive voice interfaces in applications such as directory assistance and data entry for telecommunications. Recognition of such words requires an ability to generate reasonably accurate pronunciation networks. This is a very challenging problem because a large percentage of proper nouns, such as personal names, appear to have no obvious (or simple) letter to mapping rules that can be used to generate the pronunciations. It appears to be an open-ended problem that is constantly evolving as a function of numerous sociological factors. Yet humans do amazingly well at generating and recognizing the pronunciation of a name never encountered before. We present an algorithm based on a Boltzmann machine type of neural network that generates the most likely pronunciations of a proper noun from the text-only spellings of the name. This method does not require voice data containing the spelling or nominal pronunciation