Sentence Understanding and Learning of New Words with Large-Scale Neural Networks

  • Heiner Markert
  • Zöhre Kara Kayikci
  • Günther Palm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5064)

Abstract

We have implemented a speech command system which can understand simple command sentences like “Bot lift ball” or “Bot go table” using hidden Markov models (HMMs) and associative memories with sparse distributed representations. The system is composed of three modules: (1) A set of HMMs is used on phoneme level to get a phonetic transcription of the spoken sentence, (2) a network of associative memories is used to determine the word belonging to the phonetic transcription and (3) a neural network is used on the sentence level to determine the meaning of the sentence. The system is also able to learn new object words during performance.

Keywords

Associative Memories Hidden Markov Models Hebbian Learning Speech Recognition Language Understanding 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Heiner Markert
    • 1
  • Zöhre Kara Kayikci
    • 1
  • Günther Palm
    • 1
  1. 1.Institute of Neural Information ProcessingUniversity of UlmUlmGermany

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