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ICANN ’93 pp 421-421 | Cite as

Weighted Distance Measure for Speaker-Independent Digit Recognition with Hidden-Control Neural Network

  • Dou-Seok Kim
  • Soo-Young Lee
Conference paper

Abstract

Hidden control neural network (HCNN) incorporates dynamic state transition of the hidden Markov model (HMM) with Viterbi segmentation. [1] In this paper we report a different hidden-state representation and weighted distance measure for better recognition rates.

Keywords

Hide Markov Model Synaptic Weight Diagonal Term Speech Data Viterbi Algorithm 
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.

References

  1. [1]
    E. Levin, “Modeling time varying systems using hidden control neural architecture,” Proc. NIPS-3, pp. 147–154, 1991.Google Scholar
  2. [2]
    Y. Tohkura, “A weighted cepstral distance measure for speech recognition,” IEEE Trans. ASSP, pp. 1414–1422, 1987.Google Scholar

Copyright information

© Springer-Verlag London Limited 1993

Authors and Affiliations

  • Dou-Seok Kim
    • 1
  • Soo-Young Lee
    • 1
  1. 1.Department of Electrical EngineeringKorea Advanced Institute of Science and TechnologyYusong-gu, TaejonKorea (South)

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