Model parameter estimation for mixture density polynomial segment models

T. Fukada*, K. K. Paliwal, Y. Sagisaka

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In this paper, we propose parameter estimation techniques for mixture density polynomial segment models (MDPSMs) where their trajectories are specified with an arbitrary regression order. MDPSM parameters can be trained in one of three different ways: (1) segment clustering; (2) expectation maximization (EM) training of mean trajectories; and (3) EM training of mean and variance trajectories. These parameter estimation methods were evaluated in TIMIT vowel classification experiments. The experimental results showed that modelling both the mean and variance trajectories is consistently superior to modelling only the mean trajectory. We also found that modelling both trajectories results in significant improvements over the conventional HMM.

Original languageEnglish
Pages (from-to)229-246
Number of pages18
JournalComputer Speech and Language
Issue number3
Publication statusPublished - 1998 Jun
Externally publishedYes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Human-Computer Interaction


Dive into the research topics of 'Model parameter estimation for mixture density polynomial segment models'. Together they form a unique fingerprint.

Cite this