@article{ShigaJackson_El-Lett08,
  AUTHOR   =	"Shiga, Y. and Jackson, P. J. B.",
  YEAR     =	"2008",
  TITLE    =	"Start- and End-node Segmental-{HMM} Pruning",
  JOURNAL  =	"Electronics Letters",
  VOLUME   =	"40",
  NUMBER   =	"1",
  PAGES    =	"60-61",
  CITE     =	"Shiga and Jackson (2008)",
  MONTH    =	"January",
  DOI      =    "10.1049/el:20082233",
  ABSTRACT = 	
"An efficient decoding algorithm for segmental HMMs (SHMMs) is proposed with
multi-stage pruning.  The generation by SHMMs of a feature trajectory for each 
state expands the search space and the computational cost of decoding.  We 
reduce it in three ways: pre-cost partitioning, start-node (SN) beam pruning, 
and  conventional end-node (EN) beam pruning.  Experiments show that 
partitioning cuts computation by 20-25% for supervised training, and 40-50% for 
phone classification, without  degradation in recognition accuracy; SN and EN 
beam pruning together give 80% reduction for embedded recognition on triphone 
SHMMs, with less than 0.1% degradation."
}