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Statistical Inference in MLPs

  • Hervé A. Bourlard
  • Nelson Morgan
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 247)

Abstract

In Chapter 3, we showed that HMMs were stochastic models that dealt efficiently with the statistical and sequential character of the speech signal, but which also suffer from several limiting assumptions that are required for tractable solutions. In Chapter 4, we discussed ANNs and showed that they had their own attractive properties; in particular, they appear to rely on fewer basic assumptions. Chapter 5 briefly reviewed the most popular ANN approaches currently used for sequence processing in general and speech recognition in particular. We concluded that none of these were able to solve CSR properly using ANNs by themselves. Given these tradeoffs, we have been interested in using ANNs to overcome some HMM drawbacks while staying within the latter’s formalism. This kind of hybrid is frequently not straightforward, however; for instance, it is difficult to optimally incorporate rule-based speech knowledge in an HMM-based ASR system.1

Keywords

Hide Unit Output Unit Input Field Linear Discriminant Function Classification Error Rate 
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

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • Hervé A. Bourlard
    • 1
    • 2
  • Nelson Morgan
    • 2
    • 3
  1. 1.Lernout & Hauspie Speech ProductsBelgium
  2. 2.International Computer Science InstituteBerkeleyUSA
  3. 3.University of CaliforniaBerkeleyUSA

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