  Recent publications on automatic-speech-recognition (ASR) have a strong focus
on attention encoder-decoder (AED) architectures which work well for large
datasets, but tend to overfit when applied in low resource scenarios. One
solution to tackle this issue is to generate synthetic data with a trained
text-to-speech system (TTS) if additional text is available. This was
successfully applied in many publications with AED systems. We present a novel
approach of silence correction in the data pre-processing for TTS systems which
increases the robustness when training on corpora targeted for ASR
applications. In this work we do not only show the successful application of
synthetic data for AED systems, but also test the same method on a highly
optimized state-of-the-art Hybrid ASR system and a competitive monophone based
system using connectionist-temporal-classification (CTC). We show that for the
later systems the addition of synthetic data only has a minor effect, but they
still outperform the AED systems by a large margin on LibriSpeech-100h. We
achieve a final word-error-rate of 3.3%/10.0% with a Hybrid system on the
clean/noisy test-sets, surpassing any previous state-of-the-art systems that do
not include unlabeled audio data.
