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Conclusions

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

Abstract

As of this writing (1993), it is still too early to describe the long-term impact of neural networks on future ASR systems. Many of us have been attracted to ANNs at least partly because they often are useful for problems in which we have little prior knowledge. However, for a problem that has been investigated for decades like speech recognition, it is quite difficult to improve state-of-the-art systems with such a simple approach. We have yet to develop specific instances where such a completely “ignorance-based” approach can be used to successfully solve difficult problems. However, we now have a number of examples (in addition to the one described in this book) in which neural network techniques are successfully applied to practical problems such as recognizing handwritten postal mail codes or predicting time series.1 However, progress in any of these areas still requires an extensive knowledge of relevant fields; we cannot disregard what has been achieved by more traditional techniques.

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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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