Recently, SpecAugment, an augmentation scheme for automatic speech
recognition that acts directly on the spectrogram of input utterances, has
shown to be highly effective in enhancing the performance of end-to-end
networks on public datasets. In this paper, we demonstrate its effectiveness on
tasks with large scale datasets by investigating its application to the Google
Multidomain Dataset (Narayanan et al., 2018). We achieve improvement across all
test domains by mixing raw training data augmented with SpecAugment and
noise-perturbed training data when training the acoustic model. We also
introduce a modification of SpecAugment that adapts the time mask size and/or
multiplicity depending on the length of the utterance, which can potentially
benefit large scale tasks. By using adaptive masking, we are able to further
improve the performance of the Listen, Attend and Spell model on LibriSpeech to
2.2% WER on test-clean and 5.2% WER on test-other.

本文介绍了一种基于 SpecAugment 的自适应掩蔽方法，该方法可以直接作用于语音识别中输入语音信号的高频图谱上，并证明了该方法在大规模数据集上的有效性，通过将增强数据与噪声扰动训练的数据混合使用，使得模型在所有测试领域中都取得了进一步的提升。