Uncertainty in training large vocabulary speech recognizers
Amarnag Subramanya, Chris D. Bartels, Jeff Bilmes, Patrick Nguyen · 2007
We propose a technique for annotating data used to train a speech recognizer. The proposed scheme is based on labeling only a single frame for every word in the training set. We make use of the virtual evidence (VE) framework within a graphical model to take advantage of such data. We apply this approach to a large vocabulary speech recognition task, and show that our VE-based training scheme can improve over the performance of a system trained using sequence labeled data by 2.8% and 2.1% on the dev01 and eva101 sets respectively. Annotating data in the proposed scheme is not significantly slower than sequence labeling. We present timing results showing that training using the proposed approach is about 10 times faster than training using sequence labeled data while using only about 75% of the memory.