Automatic lexical pronunciations generation and update

Abstract

Most automatic speech recognizers use a dictionary that maps words to one or more canonical pronunciations. Such entries are typically hand-written by lexical experts. In this research, we investigate a new approach for automatically generating lexical pronunciations using a linguistically motivated subword model, and refining the pronunciations with spoken examples. The approach is evaluated on an isolated word recognition task with a 2 k lexicon of restaurant and street names. A letter-to-sound model is first used to generate seed baseforms for the lexicon. Then spoken utterances of words in the lexicon are presented to a subword recognizer and the top hypotheses are used to update the lexical base-forms. The spelling of each word is also used to constrain the subword search space and generate spelling-constrained baseforms. The results obtained are quite encouraging and indicate that our approach can be successfully used to learn valid pronunciations of new words.