  Although end-to-end automatic speech recognition (E2E ASR) has achieved great
performance in tasks that have numerous paired data, it is still challenging to
make E2E ASR robust against noisy and low-resource conditions. In this study,
we investigated data augmentation methods for E2E ASR in distant-talk
scenarios. E2E ASR models are trained on the series of CHiME challenge
datasets, which are suitable tasks for studying robustness against noisy and
spontaneous speech. We propose to use three augmentation methods and thier
combinations: 1) data augmentation using text-to-speech (TTS) data, 2)
cycle-consistent generative adversarial network (Cycle-GAN) augmentation
trained to map two different audio characteristics, the one of clean speech and
of noisy recordings, to match the testing condition, and 3) pseudo-label
augmentation provided by the pretrained ASR module for smoothing label
distributions. Experimental results using the CHiME-6/CHiME-4 datasets show
that each augmentation method individually improves the accuracy on top of the
conventional SpecAugment; further improvements are obtained by combining these
approaches. We achieved 4.3\% word error rate (WER) reduction, which was more
significant than that of the SpecAugment, when we combine all three
augmentations for the CHiME-6 task.
