Virtual evidence for training speech recognizers using partially labeled data
Amarnag Subramanya, Jeff Bilmes · 2007
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). 1