Modality Confidence Aware Training for Robust End-to-End Spoken Language
  Understanding
Suyoun Kim Akshat Shrivastava Duc Le Ju Lin Ozlem Kalinli Michael L. Seltzer
End-to-end (E2E) spoken language understanding (SLU) systems that generate a
semantic parse from speech have become more promising recently. This approach
uses a single model that utilizes audio and text representations from
pre-trained speech recognition models (ASR), and outperforms traditional
pipeline SLU systems in on-device streaming scenarios. However, E2E SLU systems
still show weakness when text representation quality is low due to ASR
transcription errors. To overcome this issue, we propose a novel E2E SLU system
that enhances robustness to ASR errors by fusing audio and text representations
based on the estimated modality confidence of ASR hypotheses. We introduce two
novel techniques: 1) an effective method to encode the quality of ASR
hypotheses and 2) an effective approach to integrate them into E2E SLU models.
We show accuracy improvements on STOP dataset and share the analysis to
demonstrate the effectiveness of our approach.