DocumentCode
2491614
Title
On efficient Viterbi decoding for hidden semi-Markov models
Author
Datta, Ritendra ; Hu, Jianying ; Ray, Bonnie
Author_Institution
Penn State Univ., University Park, PA
fYear
2008
fDate
8-11 Dec. 2008
Firstpage
1
Lastpage
4
Abstract
We present algorithms for improved Viterbi decoding for the case of hidden semi-Markov models. By carefully constructing directed acyclic graphs, we pose the decoding problem as that of finding the longest path between specific pairs of nodes. We consider fully connected models as well as restrictive topologies and state duration conditions, and show that performance improves by a significant factor in all cases. Detailed algorithms as well as theoretical results related to their run times are provided.
Keywords
Viterbi decoding; computational complexity; directed graphs; hidden Markov models; optimisation; Viterbi decoding; computational complexity; directed acyclic graph; fully connected model; hidden semiMarkov model; optimisation; restrictive topology; state duration condition; Algorithm design and analysis; Decoding; Event detection; Hidden Markov models; Inference algorithms; Proteins; Speech analysis; Speech recognition; Topology; Viterbi algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location
Tampa, FL
ISSN
1051-4651
Print_ISBN
978-1-4244-2174-9
Electronic_ISBN
1051-4651
Type
conf
DOI
10.1109/ICPR.2008.4761926
Filename
4761926
Link To Document