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

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