Large-Scale Unsupervised Pre-Training for End-to-End Spoken Language Understanding

Abstract

End-to-end Spoken Language Understanding (SLU) is proposed to infer the semantic meaning directly from audio features without intermediate text representation. In this paper, we explore unsupervised pre-training for End-to-end SLU models by learning representations from large-scale raw audios. The pre-trained model preserves semantic features which benefit the downstream SLU tasks as the learned model weights are further fine-tuned on the task specific training data. Our approach out-perform the state-of-the-art end-to-end SLU system with over 18.33% error reduction.