A model of lexical access from partial phonetic information

Current approaches to isolated word recognition rely on classical pattern recognition techniques which utilize little or no speech specific knowledge. While the performance of these systems is quite good, they are not readily extensible to tasks involving very large vocabularies and many different speakers. This paper presents a model of lexical access using partial phonetic information. Rather than performing detailed phonetic analysis, a word is characterized in terms of broad phonetic and prosodic information. This partial description is then used to retrieve a small set of words from a large lexicon. The broad class representation used in the model is both relatively insensitive to variability in the speech signal, and very powerful in differentiating among the words in a large lexicon. In order to evaluate the use of this model, we have implemented a word hypothesizer which uses partial phonetic information in lexical access. The system performs a broad phonetic categorization of the acoustic signal. This broad classification is used to return a small set of word candidates from a 20,000 word lexicon. The system is not trained to a specific speaker or vocabulary.