  Inspired by SpecAugment -- a data augmentation method for end-to-end ASR
systems, we propose a frame-level SpecAugment method (f-SpecAugment) to improve
the performance of deep convolutional neural networks (CNN) for hybrid HMM
based ASR systems. Similar to the utterance level SpecAugment, f-SpecAugment
performs three transformations: time warping, frequency masking, and time
masking. Instead of applying the transformations at the utterance level,
f-SpecAugment applies them to each convolution window independently during
training. We demonstrate that f-SpecAugment is more effective than the
utterance level SpecAugment for deep CNN based hybrid models. We evaluate the
proposed f-SpecAugment on 50-layer Self-Normalizing Deep CNN (SNDCNN) acoustic
models trained with up to 25000 hours of training data. We observe
f-SpecAugment reduces WER by 0.5-4.5% relatively across different ASR tasks for
four languages. As the benefits of augmentation techniques tend to diminish as
training data size increases, the large scale training reported is important in
understanding the effectiveness of f-SpecAugment. Our experiments demonstrate
that even with 25k training data, f-SpecAugment is still effective. We also
demonstrate that f-SpecAugment has benefits approximately equivalent to
doubling the amount of training data for deep CNNs.
