Hidden Markov model and neural network hybrid

Research output: Chapter in Book/Report/Conference proceedingConference contribution


When there is a mismatch between training and testing environments, statistical pattern classification methods may suffer from severe degradation in their performance because the parameters in the classifiers do not represent the testing data well. The mismatch is typically due to the interference or noises from operating environments. In this paper, a neural network based transformation approach is studied to handle the distribution mismatches between training and testing data. The probability density functions of the statistical classifiers are used as the objective function of the neural network. The neural network maximizes the likelihood of the data from a testing environment, and allows global optimization of the network when used with the statistical pattern classifiers. The proposed approach is applied to the area of automatic speech recognition to recognize noisy distant-talking speech and it reduces the error rate by 52.9%.

Original languageEnglish
Title of host publicationEurAsia-ICT 2002
Subtitle of host publicationInformation and Communication Technology - First EurAsian Conference, Proceedings
EditorsHassan Shafazand, A. Min Tjoa, Hassan Shafazand
PublisherSpringer Verlag
Number of pages8
ISBN (Print)3540000283, 9783540000280
Publication statusPublished - 2002
Event1st EurAsian Conference on Advances in Information and Communication Technology, EurAsia-ICT 2002 - Shiraz, Iran, Islamic Republic of
Duration: 2002 Oct 292002 Oct 31

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2510 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other1st EurAsian Conference on Advances in Information and Communication Technology, EurAsia-ICT 2002
Country/TerritoryIran, Islamic Republic of

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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