Using Speech Synthesis to Train End-To-End Spoken Language Understanding Models

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. We propose a strategy to overcome this requirement in which speech synthesis is used to generate a large synthetic training dataset from several artificial speakers. We confirm the effectiveness of our approach with experiments on two open-source SLU datasets, where synthesized speech is used both as a sole source of training data and as a form of data augmentation.