Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures

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.