Virtual evidence for training speech recognizers using partially labeled data

  • Authors:

  • Amarnag Subramanya;Jeff Bilmes

  • Affiliations:

  • University of Washington, Seattle, WA;University of Washington, Seattle, WA

  • Venue:
  • NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
  • Year:
  • 2007

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Abstract

Collecting supervised training data for automatic speech recognition (ASR) systems is both time consuming and expensive. In this paper we use the notion of virtual evidence in a graphical-model based system to reduce the amount of supervisory training data required for sequence learning tasks. We apply this approach to a TIMIT phone recognition system, and show that our VE-based training scheme can, relative to a baseline trained with the full segmentation, yield similar results with only 15.3% of the frames labeled (keeping the number of utterances fixed).