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Learning diphone-based segmentation.

Robert Daland1, Janet B Pierrehumbert

  • 1Department of Linguistics, UCLA, Los Angeles, CA 90095-1543, USA. rdaland@humnet.ucla.edu

Cognitive Science
|March 25, 2011
PubMed
Summary

This study develops a learnable model for word segmentation, showing infants can learn word boundaries from limited language exposure. The model

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Area of Science:

  • Computational Linguistics
  • Developmental Psychology
  • Speech Processing

Background:

  • Previous diphone-based word segmentation models were considered unlearnable.
  • Infant language acquisition relies on identifying word boundaries.

Purpose of the Study:

  • To develop a statistically principled, learnable word segmentation model.
  • To test the model's ability to recover phrase-medial word boundaries using real-world phonetic data.
  • To investigate the impact of limited language exposure on segmentation performance.

Main Methods:

  • Utilized Bayes' theorem and assumptions of infants' implicit knowledge.
  • Developed unsupervised and semi-supervised learning models.
  • Tested models on phonetic corpora from child-adult interactions.

Main Results:

  • Achieved ceiling performance with 1 day to 1 month of language exposure.
  • Demonstrated robustness to parameter and input representation variations.
  • Observed undersegmentation in both learning and baseline models.

Conclusions:

  • The developed model is learnable and effective for word segmentation.
  • Limited language exposure is sufficient for infants to learn word boundaries.
  • Undersegmentation has significant implications for overall speech processing.

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