An intuitive model of perceptual grouping for HCI design
Ruth Rosenholtz, Nathaniel R. Twarog, et al.
CHI EA 2009
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 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. © 2003 Elsevier Ltd. All rights reserved.
Ruth Rosenholtz, Nathaniel R. Twarog, et al.
CHI EA 2009
Jean McKendree, John M. Carroll
CHI 1986
Claudio Santos Pinhanez, Edem Wornyo
CHI 2025
Elaine Hill
Human-Computer Interaction