title: Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures
abstract: Recent publications on automatic-speech-recognition (ASR) have a strong focus on attention encoder-decoder (AED) architectures which tend to suffer from over-fitting 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, but only very limited in the context of other ASR architectures. We investigate the effect of vary
journal: 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)