A Streaming End-to-End Framework For Spoken Language Understanding

Understand

End-to-end spoken language understanding (SLU) has recently attracted increasing interest.

• Compared to the conventional tandem-based approach that combines speech recognition and language understanding as separate modules, the new approach extracts users' intentions directly from the speech signals, resulting in joint optimization and low latency.
• Such an approach, however, is typically designed to process one intention at a time, which leads users to take multiple rounds to fulfill their requirements while interacting with a dialogue system.
• In this paper, we propose a streaming end-to-end framework that can process multiple intentions in an online and incremental way.