Recent approaches to large vocabulary decoding with weighted finite-state transducers have focused on the use of determinization and minimization algorithms to produce compact decoding graphs. This paper addresses the problem of compiling decoding graphs with long span cross-word context dependency between acoustic models. To this end, we extend the finite state approach by developing complementary arc factorization techniques that operate on non-deterministic graphs. The use of these techniques allows us to statically compile decoding graphs in which the acoustic models utilize a full word of cross-word context. This is in significant contrast to typical systems which use only a single phone. WE show that the particular arc-minimization problem that arises is in fact an a NP-complete combinatorial optimization problem. Heuristics for this problem are then presented, and are used in experiments on a Switchboard task, illustrating the moderate sizes and runtimes of the graphs we build.<\/p>\n","protected":false},"excerpt":{"rendered":"
Recent approaches to large vocabulary decoding with weighted finite-state transducers have focused on the use of determinization and minimization algorithms to produce compact decoding graphs. This paper addresses the problem of compiling decoding graphs with long span cross-word context dependency between acoustic models. To this end, we extend the finite state approach by developing complementary […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"gzweig"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Computer Speech and Language. 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