Fusing length and voicing information, and HMM decision using a Bayesian Causal Tree against insufficient training data


Demirekler M., Karahan F., Ciloglu T.

15th International Conference on Pattern Recognition (ICPR-2000), Barcelona, Spain, 3 - 07 September 2000, pp.102-105 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • City: Barcelona
  • Country: Spain
  • Page Numbers: pp.102-105

Abstract

This paper presents the work done to improve the recognition rate lit an isolated word recognition problem with single utterance training. The negative effect of en ors (due to insufficient training data) in estimated model parameter is compensated by fusing the information obtained fi om HMM evaluation and those generated for the word length and voicing at the beginning and end of the word. A Bayesian Causal Tree structure is developed to accomplish the fusion. The final decision is made on one of the three candidates which ale most likely according to HMM evaluation. The reliability of the HMM ordering is improved by applying variance flooring.