Temporal Hidden Markov Models

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

1 Citation (Scopus)
96 Downloads (Pure)

Abstract

The hidden Markov model (HMM) is a double stochastic process. The observable process produces a sequence of observations and the hidden process is a Markov process. The HMM assumes that the occurrence of one observation is statistically independent of the occurrence of the others. To avoid this limitation, a temporal HMM is proposed. The hidden process in the temporal HMM is the same, but the observable process is now a Markov process. Each observation in the training sequence is assumed to be statistically dependent on its predecessor, and codewords or Gaussian components are used as states in the observable Markov process. Speaker identification experiments performed on 138 Gaussian mixture speaker models in the YOHO database shows a better performance for the temporal HMM compared to the standard HMM
Original languageEnglish
Title of host publicationProceedings of the 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing
EditorsJ Kwok, LM Po
Place of PublicationHong Kong
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages137-140
Number of pages4
ISBN (Print)0-7803-8688-4
Publication statusPublished - 2004
Event2004 International Symposium on Intelligent Multimedia, Video and Speech Processing - , Hong Kong
Duration: 19 Oct 200421 Oct 2004

Conference

Conference2004 International Symposium on Intelligent Multimedia, Video and Speech Processing
Country/TerritoryHong Kong
Period19/10/0421/10/04

Fingerprint

Dive into the research topics of 'Temporal Hidden Markov Models'. Together they form a unique fingerprint.

Cite this