DocumentCode
968224
Title
Reducing redundant computation in HMM evaluation
Author
Deller, J.R., Jr. ; Snider, R.K.
Author_Institution
Dept. of Electr. Eng., Michigan State Univ., East Lansing, MI, USA
Volume
1
Issue
4
fYear
1993
fDate
10/1/1993 12:00:00 AM
Firstpage
465
Lastpage
471
Abstract
Redundant computations occur when a set of hidden Markov models (HMMs) is evaluated with respect to an observation string. A formal restructuring of the HMM allows the redundancy to be identified and removed. An 𝒪([1-κ]NT) complexity HMM results, with the compression index κ ∈ [0,1) depending upon several factors. Isolated-word recognition experiments illustrate the results
Keywords
computational complexity; data compression; hidden Markov models; redundancy; speech coding; speech recognition; HMM evaluation; complexity; compression index; hidden Markov models; isolated-word recognition experiments; observation string; redundant computation reduction; Databases; Hidden Markov models; Linear predictive coding; Poles and zeros; Predictive models; Quantization; Reflection; Speech analysis; Speech coding; Speech synthesis;
fLanguage
English
Journal_Title
Speech and Audio Processing, IEEE Transactions on
Publisher
ieee
ISSN
1063-6676
Type
jour
DOI
10.1109/89.242493
Filename
242493
Link To Document