Semi-Supervised Methods for Improving Keyword Search of Unseen Terms

We present a semi-supervised language modeling technique to improve search performance on terms without training data. Probabilities estimated from automatic transcripts of a large corpus of in-domain audio are added to an existing LM. Requiring neither development data or external resources, our method achieves 70% of the possible gain for manual transcription of the same audio. This is in sharp contrast to the modest gains of previous semi-supervised LM experiments. We compare the value of additional resources (labor or data) to semi-supervised learning. If human effort is available, we describe a transcription regime to efficiently close the remaining performance gap.