  In training speech recognition systems, labeling audio clips can be
expensive, and not all data is equally valuable. Active learning aims to label
only the most informative samples to reduce cost. For speech recognition,
confidence scores and other likelihood-based active learning methods have been
shown to be effective. Gradient-based active learning methods, however, are
still not well-understood. This work investigates the Expected Gradient Length
(EGL) approach in active learning for end-to-end speech recognition. We justify
EGL from a variance reduction perspective, and observe that EGL's measure of
informativeness picks novel samples uncorrelated with confidence scores.
Experimentally, we show that EGL can reduce word errors by 11\%, or
alternatively, reduce the number of samples to label by 50\%, when compared to
random sampling.
