STOP: A dataset for Spoken Task Oriented Semantic Parsing

Understand

End-to-end spoken language understanding (SLU) predicts intent directly from audio using a single model.

• It promises to improve the performance of assistant systems by leveraging acoustic information lost in the intermediate textual representation and preventing cascading errors from Automatic Speech Recognition (ASR).
• Further, having one unified model has efficiency advantages when deploying assistant systems on-device.
• However, the limited number of public audio datasets with semantic parse labels hinders the research progress in this area.