3. The method according to claim 1, further comprising the steps of: replacing arcs in said word pronunciation networks with hidden Markov models of phones; estimating counts of traversals of each transition of a forward-backward algorithm for each training sample, wherein transitions into initial nodes of hidden Markov model phone models correspond to arcs in word pronunciation networks; and after combining counts for nodes in equivalence classes, employing said combined counts for estimating probabilities of each of the outgoing arcs from each node in the word pronunciation networks.
3. The method according to claim 1, further comprising the steps of: replacing arcs in said word pronunciation networks with hidden Markov models of phones; estimating counts of traversals of each transition of a forward-backward algorithm for each training sample, wherein transitions into initial nodes of hidden Markov model phone models correspond to arcs in word pronunciation networks; and after combining counts for nodes in equivalence classes, employing said combined counts for estimating probabilities of each of the outgoing arcs from each node in the word pronunciation networks. 4. The method according to claim 3, wherein said estimating step is given by: ##EQU4## where P.sub.i,j =estimated probability for arc i in equivalence class j; C.sub.i,k =forward-backward count for arc i leaving node k.