A novel divergence measure for hidden Markov models

Author

Mohammad, Maruf ; Tranter, W.H.

Author_Institution

Mobile & Portable Radio Res. Group, Virginia Tech, Blacksburg, VA, USA

fYear

2005

fDate

8-10 April 2005

Firstpage

240

Lastpage

243

Abstract

In this paper, a novel divergence measure for hidden Markov models (HMMs) is introduced. The widely used distance measure between two HMMs is the Kullback-Leibler divergence (KLD). The Monte-Carlo method is usually applied to calculate the KLD, whose computational complexity is prohibitive in practical applications. Numerical examples show that the proposed divergence measure closely approximates the KLD with a saving of hundreds of times in computational complexity.

Keywords

computational complexity; hidden Markov models; HMM divergence measure; KLD; Kullback-Leibler divergence; Monte-Carlo method; computational complexity; hidden Markov models; Automatic speech recognition; Computational complexity; Hidden Markov models; Iterative algorithms; Probability distribution; Signal processing algorithms; Speech recognition; Statistics; Upper bound; Vocabulary;

fLanguage

English

Publisher

ieee

Conference_Titel

SoutheastCon, 2005. Proceedings. IEEE

Print_ISBN

0-7803-8865-8

Type

conf

DOI

10.1109/SECON.2005.1423253

Filename

1423253

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3082794