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

Dynamic curriculum learning via data parameters for noise robust keyword spotting