A multipass recognition strategy selects the N-best hypotheses resulting from each pass and propagates these N-best to the next pass. This strategy outperforms conventional hidden Markov model recognizers using a grammar constraining all possible names. Real time recognition of continuously spelled names is made feasible, in part, because the processor-intensive costly constraints are applied, if at all, in the 4th pass, after the system has produced a much smaller dynamic grammar.
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