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Active Learning for Speech Recognition: the Power of Gradients
[article]
2016
arXiv
pre-print
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
arXiv:1612.03226v1
fatcat:ew7idft6sjcpzdrrtqg3qanz6q