DocumentCode
1060254
Title
Kneser–Ney Smoothing With a Correcting Transformation for Small Data Sets
Author
Taraba, Peter
Author_Institution
Smart Desktop, Seattle
Volume
15
Issue
6
fYear
2007
Firstpage
1912
Lastpage
1921
Abstract
We present a technique which improves the Kneser-Ney smoothing algorithm on small data sets for bigrams, and we develop a numerical algorithm which computes the parameters for the heuristic formula with a correction. We give motivation for the formula with correction on a simple example. Using the same example, we show the possible difficulties one may run into with the numerical algorithm. Applying the algorithm to test data we show how the new formula improves the results on cross-entropy.
Keywords
entropy; maximum likelihood estimation; optimisation; probability; smoothing methods; speech recognition; Kneser-Ney smoothing algorithm; bigrams; correcting transformation; cross-entropy; heuristic formula; maximum-likelihood estimation; numerical algorithm; probability; small data sets; speech processing; speech recognition; Character recognition; Entropy; Handwriting recognition; Maximum likelihood detection; Maximum likelihood estimation; Optical character recognition software; Smoothing methods; Speech recognition; Testing; Speech processing; speech recognition;
fLanguage
English
Journal_Title
Audio, Speech, and Language Processing, IEEE Transactions on
Publisher
ieee
ISSN
1558-7916
Type
jour
DOI
10.1109/TASL.2007.900090
Filename
4276766
Link To Document