7. A speech recognition process comprising a statistical learning technique that uses a language model produced by integrating a finite state grammar probability and an n-gram probability with the finite state grammar probability being subordinated to the n-gram probability, wherein the language model comprises a probability that is specified by the equation ##EQU6## where, w.sub.q is the word about to be predicted, H.sub.q is the admissible history for this particular word, G is the probabilistic finite state grammar, S.sub.q is the present sentence up to w.sub.q, and V is the vocabulary to which w.sub.q belongs.
8. The process of claim 7, wherein at least one acoustic model is used, the at least one acoustic model used with the language model in executing a hypothesis search on an acoustic sequence to provide a word sequence output.