Elsevier

Pattern Recognition

Volume 28, Issue 1, January 1995, Pages 53-57
Pattern Recognition

Robust estimation of HMM parameters using fuzzy vector quantization and Parzen's window

https://doi.org/10.1016/0031-3203(94)00075-WGet rights and content

Abstract

The paper presents a new parameter estimation method for discrete hidden Markov models (HMM) in speech recognition. This method makes use of fuzzy vector quantization and the Parzen window in dealing with the problem of insufficient training data, and it therefore may be regarded as a kind of maximum likelihood estimation with some smoothing effect. The proposed method is compared with the existing smoothing techniques by experiments of speaker-independent isolated word recognition. The results show that the new estimation method has led to improved recognition and therefore it may be used as an alternative to the parameter smoothing techniques for HMMs.

References (7)

  • E.H. Ruspini

    Numerical methods for fuzzy clustering

    Information Sciences

    (1970)
  • S.E. Levinson et al.

    An introduction to the application of the theory of probabilistic function of a Markov process to automatic speech recognition

    The Bell System Technical J.

    (1983)
  • K.F. Lee

    Large-vocabulary speaker-independent continuous speech recognition: the SPHINX system

There are more references available in the full text version of this article.

Cited by (0)

This work was carried out at the Robert Gordon University, Aberdeen, U.K.

View full text