Since the finite-state framework selects the best path over the whole utterance, it also allows for optimization over a sequence of decisions, rather than optimizing each local decision separately. 6 Unlike Belz (1998) , where the actual FSM structure (including classes of phonemes that could be group onto one arc) was learned, here the structure of each FSM is determined in advance. Only the weight on each arc is derived from data. No attempt is made to combine phonemes to produce more minimal FSMs; each phoneme (and phoneme-pair) is modeled separately. Like<cite> Brent (1999a)</cite> and indeed most models in the literature, this model assumes (for sake of convenience and simplicity) that the child hears each segment produced within an utterance without error. This assumption translates into the finitestate domain as a simple acceptor (or equivalently, an identity transducer) over the segment sequence for a given utterance.