Abstract
We propose a novel method to obtain the -best
list of hypotheses in hidden Markov model (HMM).
We show that the entire information needed to compute
the -best list from the HMM trellis graph
is encapsulated in entities that can be computed
in a single forward-backward iteration that usually
yields the most likely state sequence. The hypotheses
list can then be extracted in a sequential manner
from these entities without the need to refer back
to the original data of the HMM. Furthermore, our
approach can yield significant savings of computational
time when compared to traditional methods
Original language | American English |
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Title of host publication | International Conference on Artificial Intelligence (IJCAI) |
State | Published - 2001 |