1 citations · 2 across the 6 of their papers we have counts for
3 papers · 1 filter
PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding
Trang Le, Daniel Lazar, Suyoun Kim +6
Spoken Language Understanding (SLU) is a critical component of voice assistants; it consists of converting speech to semantic parses for task execution. Previous works have explore…
Augmenting text for spoken language understanding with Large Language Models
Roshan Sharma, Suyoun Kim, Daniel Lazar +7
Spoken semantic parsing (SSP) involves generating machine-comprehensible parses from input speech. Training robust models for existing application domains represented in training d…
STOP: A dataset for Spoken Task Oriented Semantic Parsing
Paden Tomasello, Akshat Shrivastava, Daniel Lazar +12
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 a…