On the use of stochastic inference networks for representing multiple word pronunciations

New techniques are introduced to manage speaker variability in large-vocabulary speaker-independent automatic speech recognition systems. Stochastic inference networks are used to model speaker intentions and automatically derive multiple phonemic transcriptions of words. The statistical inferences are generalized and managed by a justification maintenance system module integrated with the speech module, to dynamically adapt the system´s performance to the variability of multi-speaker use. Additionally, lexical access to the database of phonemic transcriptions is mediated in multi-pass search with a new method of rejection based on beam-search derived lattices of pseudo-syllabic segments