Modeling uncertain speech sequences using type-2 fuzzy hidden Markov models

  • Authors:

  • Xiao-Qin Cao;Jia Zeng;Hong Yan

  • Affiliations:

  • Department of Electronic Engineering, City University of Hong Kong, P.R. China;Department of Electronic Engineering, City University of Hong Kong, P.R. China;Department of Electronic Engineering, City University of Hong Kong, P.R. China

  • Venue:
  • PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
  • Year:
  • 2007

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Abstract

The automatic speech recognizor (ASR) based on hidden Markov models (HMMs) is very sensitive to multi-talker, non-stationary babble noise, which consists of a large number of speakers talking simultaneously. One major reason is due to mismatches between the training and testing conditions, which makes the accurate parameters of the HMM incapable of describing the uncertain distributions of the observations in speech signals. This paper applies one extension of the HMM referred to as the type-2 fuzzy hidden Markov models (T2 FHMMs) to modeling uncertain speech sequences. More specifically, we use the type- 2 fuzzy set (T2 FS) to describe uncertain parameters of the HMM that may vary anywhere in an interval with uniform possibilities. As a result, the likelihood of the T2 FHMM becomes an interval rather than a precise real number, which can be processed by the generalized linear model (GLM) for final classification decision-making. Experimental results of phoneme classification in the babble noise demonstrate a significant improvement compared with the HMM in terms of the robustness and classification rate.