Type-2 Fuzzy Hidden Moarkov Models

Chapter
First Online:
Part of the Studies in Computational Intelligence book series (SCI, volume 591)

Abstract

This chapter extends hidden Markov models (HMMs) to type-2 fuzzy HMMs (T2 FHMMs). We derive the T2 fuzzy forward-backward algorithm and Viterbi algorithm using T2 FS operations. To investigate the effectiveness of T2 FHMMs, we apply them to phoneme classification and recognition on the TIMIT speech database. Experimental results show that T2 FHMMs can effectively handle noise and dialect uncertainties in speech signals besides a better classification performance than the classical HMMs. We also find that the larger area of the FOU in T2 FHMMs with uncertain mean vectors performs better in classification when the signal-to-noise ratio is lower.

Keywords

Hide Markov Model Viterbi Algorithm Membership Grade Automatic Speech Recognition System Phoneme Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Tsinghua University Press, Beijing and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.School of Creative MediaCity University of Hong KongHong KongChina

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