  We propose dynamic curriculum learning via data parameters for noise robust
keyword spotting. Data parameter learning has recently been introduced for
image processing, where weight parameters, so-called data parameters, for
target classes and instances are introduced and optimized along with model
parameters. The data parameters scale logits and control importance over
classes and instances during training, which enables automatic curriculum
learning without additional annotations for training data. Similarly, in this
paper, we propose using this curriculum learning approach for acoustic
modeling, and train an acoustic model on clean and noisy utterances with the
data parameters. The proposed approach automatically learns the difficulty of
the classes and instances, e.g. due to low speech to noise ratio (SNR), in the
gradient descent optimization and performs curriculum learning. This curriculum
learning leads to overall improvement of the accuracy of the acoustic model. We
evaluate the effectiveness of the proposed approach on a keyword spotting task.
Experimental results show 7.7% relative reduction in false reject ratio with
the data parameters compared to a baseline model which is simply trained on the
multiconditioned dataset.
