State mixture modelling applied to speech recognition

Dat Tran, Michael Wagner, Tongtao Zheng

Research output: Contribution to journalArticlepeer-review


In state mixture modelling (SMM), the temporal structure of the observation sequences is represented by the state joint probability distribution where mixtures of states are considered. This technique is considered in an iterative scheme via maximum likelihood estimation. A fuzzy estimation approach is also introduced to cooperate with the SMM model. This new approach not only saves calculations from 2NTT (HMM direct calculation) and N2T (Forward-backward algorithm) to just only 2NT calculations, but also achieves a better recognition result.

Original languageEnglish
Pages (from-to)1449-1456
Number of pages8
JournalPattern Recognition Letters
Issue number11-13
Publication statusPublished - Nov 1999
EventProceedings of the 1999 Pattern Recognition in Practice (PRP VI) - Vlieland, Neth
Duration: 2 Jun 19994 Jun 1999


Dive into the research topics of 'State mixture modelling applied to speech recognition'. Together they form a unique fingerprint.

Cite this