Speech To Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces
This work addresses SLU for voice assistants, enabling offline, privacy-preserving deployment with reduced server costs, though it appears incremental as it builds on existing ASR and NLU methods.
The paper tackles the problem of spoken language understanding (SLU) by developing an end-to-end joint model that integrates automatic speech recognition (ASR) and natural language understanding (NLU) via neural interfaces, showing improvements in both ASR and NLU performance.
We consider the problem of spoken language understanding (SLU) of extracting natural language intents and associated slot arguments or named entities from speech that is primarily directed at voice assistants. Such a system subsumes both automatic speech recognition (ASR) as well as natural language understanding (NLU). An end-to-end joint SLU model can be built to a required specification opening up the opportunity to deploy on hardware constrained scenarios like devices enabling voice assistants to work offline, in a privacy preserving manner, whilst also reducing server costs. We first present models that extract utterance intent directly from speech without intermediate text output. We then present a compositional model, which generates the transcript using the Listen Attend Spell ASR system and then extracts interpretation using a neural NLU model. Finally, we contrast these methods to a jointly trained end-to-end joint SLU model, consisting of ASR and NLU subsystems which are connected by a neural network based interface instead of text, that produces transcripts as well as NLU interpretation. We show that the jointly trained model shows improvements to ASR incorporating semantic information from NLU and also improves NLU by exposing it to ASR confusion encoded in the hidden layer.