  End-to-end models are an attractive new approach to spoken language
understanding (SLU) in which the meaning of an utterance is inferred directly
from the raw audio without employing the standard pipeline composed of a
separately trained speech recognizer and natural language understanding module.
The downside of end-to-end SLU is that in-domain speech data must be recorded
to train the model. In this paper, we propose a strategy for overcoming this
requirement in which speech synthesis is used to generate a large synthetic
training dataset from several artificial speakers. Experiments on two
open-source SLU datasets confirm the effectiveness of our approach, both as a
sole source of training data and as a form of data augmentation.
