  The goal of this paper is to simulate the benefits of jointly applying active
learning (AL) and semi-supervised training (SST) in a new speech recognition
application. Our data selection approach relies on confidence filtering, and
its impact on both the acoustic and language models (AM and LM) is studied.
While AL is known to be beneficial to AM training, we show that it also carries
out substantial improvements to the LM when combined with SST. Sophisticated
confidence models, on the other hand, did not prove to yield any data selection
gain. Our results indicate that, while SST is crucial at the beginning of the
labeling process, its gains degrade rapidly as AL is set in place. The final
simulation reports that AL allows a transcription cost reduction of about 70%
over random selection. Alternatively, for a fixed transcription budget, the
proposed approach improves the word error rate by about 12.5% relative.
