  In this work we evaluate the utility of synthetic data for training automatic
speech recognition (ASR). We use the ASR training data to train a
text-to-speech (TTS) system similar to FastSpeech-2. With this TTS we reproduce
the original training data, training ASR systems solely on synthetic data. For
ASR, we use three different architectures, attention-based encoder-decoder,
hybrid deep neural network hidden Markov model and a Gaussian mixture hidden
Markov model, showing the different sensitivity of the models to synthetic data
generation. In order to extend previous work, we present a number of ablation
studies on the effectiveness of synthetic vs. real training data for ASR. In
particular we focus on how the gap between training on synthetic and real data
changes by varying the speaker embedding or by scaling the model size. For the
latter we show that the TTS models generalize well, even when training scores
indicate overfitting.
