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Research on Language and Computation

, Volume 8, Issue 2–3, pp 133–168 | Cite as

A Computational Model of Unsupervised Speech Segmentation for Correspondence Learning

  • Daniel Duran
  • Hinrich Schütze
  • Bernd Möbius
  • Michael Walsh
Article

Abstract

In this paper, we develop a new conceptual framework for an important problem in language acquisition, the correspondence problem: the fact that a given utterance has different manifestations in the speech and articulation of different speakers and that the correspondence of these manifestations is difficult to learn. We put forward the Correspondence-by-Segmentation Hypothesis, which states that correspondence is primarily learned by first segmenting speech in an unsupervised manner and then mapping the acoustics of different speakers onto each other. We show that a rudimentary segmentation of speech can be learned in an unsupervised fashion. We then demonstrate that, using the previously learned segmentation, different instances of a word can be mapped onto each other with high accuracy when trained on utterance-label pairs for a small set of words.

Keywords

Language acquisition Speech Perception Production Correspondence learning Segmentation 

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Daniel Duran
    • 1
  • Hinrich Schütze
    • 1
  • Bernd Möbius
    • 1
  • Michael Walsh
    • 1
  1. 1.Institute for Natural Language ProcessingUniversity of StuttgartStuttgartGermany

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