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Recently there have been efforts to introduce new benchmark tasks for spokenlanguage understanding (SLU), like semantic parsing. In this paper, we describeour proposed spoken semantic parsing system for the quality track (Track 1) inSpoken Language Understanding Grand Challenge which is part of ICASSP SignalProcessing Grand Challenge 2023. We experiment with both end-to-end andpipeline systems for this task. Strong automatic speech recognition (ASR)models like Whisper and pretrained Language models (LM) like BART are utilizedinside our SLU framework to boost performance. We also investigate the outputlevel combination of various models to get an exact match accuracy of 80.8,which won the 1st place at the challenge.

A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge