Comparing the Benefit of Synthetic Training Data for Various Automatic\n Speech Recognition Architectures
Journal: arXiv (Cornell University)
Year: 2021

Recent publications on automatic-speech-recognition (ASR) have a strong focus\non attention encoder-decoder (AED) architectures which tend to suffer from\nover-fitting in low resource scenarios. One solution to tackle this issue is to\ngenerate synthetic data with a trained text-to-speech system (TTS) if\nadditional text is available. This was successfully applied in many\npublications with AED systems, but only very limited in the context of other\nASR architectures. We investigate the effect o