Stochastic lexicon modeling for speech recognition

To optimally cope with continuous speech recognizer, we propose the stochastic lexicon model that effectively represents variations in pronunciation. In this lexicon model, the baseform of a word is represented by subword-states with a probability distribution of subword units as a two-level hidden Markov model (HMM) and this baseform is automatically trained by sample utterances. Also, the proposed approach can be applied to systems employing nonlinguistic recognition units.