Sequential End-to-End Intent and Slot Label Classification and Localization

Understand

Human-computer interaction (HCI) is significantly impacted by delayed responses from a spoken dialogue system.

• Hence, end-to-end (e2e) spoken language understanding (SLU) solutions have recently been proposed to decrease latency.
• Such approaches allow for the extraction of semantic information directly from the speech signal, thus bypassing the need for a transcript from an automatic speech recognition (ASR) system.
• In this paper, we propose a compact e2e SLU architecture for streaming scenarios, where chunks of the speech signal are processed continuously to predict intent and slot values.