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Towards ASR robust spoken language understanding through in-context learning with word confusion networks

Citation Author(s):
Kevin Everson,Yi Gu,Huck Yang,Prashanth Gurunath Shivakumar,Guan-Ting Lin,Jari Kolehmainen,Ivan Bulyko,Ankur Gandhe,Shalini Ghosh,Wael Hamza,Hung-yi Lee,Ariya Rastrow,Andreas Stolcke
Submitted by:
Kevin Everson
Last updated:
6 June 2024 - 10:22am
Document Type:
Presentation Slides
Document Year:
Kevin Everson
Paper Code:

In the realm of spoken language understanding (SLU), numerous natural language understanding (NLU) methodologies have been adapted by supplying large language models (LLMs) with transcribed speech instead of conventional written text. In real-world scenarios, prior to input into an LLM, an automated speech recognition (ASR) system generates an output transcript hypothesis, where inherent errors can degrade subsequent SLU tasks. Here we introduce a method that utilizes the ASR system’s lattice output instead of relying solely on the top hypothesis, aiming to encapsulate speech ambiguities and enhance SLU outcomes. Our in-context learning experiments, covering spoken question answering and intent classification, underline the LLM’s resilience to noisy speech transcripts with the help of word confusion networks from lattices, bridging the SLU performance gap between using the top ASR hypothesis and an oracle upper bound. Additionally, we delve into the LLM’s robustness to varying ASR performance conditions and scrutinize the aspects of in-context learning which prove the most influential.

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