End-to-End Spoken Language Understanding using RNN-Transducer ASR

• URL: http://arxiv.org/abs/2106.15919v1
• Date: Wed, 30 Jun 2021 09:20:32 GMT
• Title: End-to-End Spoken Language Understanding using RNN-Transducer ASR
• Authors: Anirudh Raju, Gautam Tiwari, Milind Rao, Pranav Dheram, Bryan Anderson, Zhe Zhang, Bach Bui, Ariya Rastrow
• Abstract summary: We propose an end-to-end trained spoken language understanding (SLU) system that extracts transcripts, intents and slots from an input speech utterance. It consists of a streaming recurrent neural network transducer (RNNT) based automatic speech recognition (ASR) model connected to a neural natural language understanding (NLU) model through a neural interface.
• Score: 14.267028645397266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an end-to-end trained spoken language understanding (SLU) system that extracts transcripts, intents and slots from an input speech utterance. It consists of a streaming recurrent neural network transducer (RNNT) based automatic speech recognition (ASR) model connected to a neural natural language understanding (NLU) model through a neural interface. This interface allows for end-to-end training using multi-task RNNT and NLU losses. Additionally, we introduce semantic sequence loss training for the joint RNNT-NLU system that allows direct optimization of non-differentiable SLU metrics. This end-to-end SLU model paradigm can leverage state-of-the-art advancements and pretrained models in both ASR and NLU research communities, outperforming recently proposed direct speech-to-semantics models, and conventional pipelined ASR and NLU systems. We show that this method improves both ASR and NLU metrics on both public SLU datasets and large proprietary datasets.

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