Large language models (LLMs) excel at text-based natural language processing tasks through creative prompt engineering and in-context learning. However, their performance on spoken language understanding (SLU) tasks relies heavily on speech-to-text conversion by an off-the-shelf automation speech recognition (ASR) system, constraining their accuracy in this setup.
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Leveraging Large Language Models for Exploiting ASR Uncertainty
With the help of creative prompt engineering and in-context learning, large language models (LLMs) are known to generalize well on a variety of text-based natural language processing (NLP) tasks. However, for performing well on spoken language understanding (SLU) tasks, LLMs either need to be equipped with in-built speech modality or they need to rely on speech-to-text conversion from an off-the-shelf automation speech recognition (ASR) system. In this work, we focus on the latter setup where the accuracy of LLM on SLU tasks is constrained by the accuracy of a frozen ASR system on the given…
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