Prompting Large Language Models for Zero-Shot Domain Adaptation in Speech Recognition

• URL: http://arxiv.org/abs/2306.16007v1
• Date: Wed, 28 Jun 2023 08:29:00 GMT
• Title: Prompting Large Language Models for Zero-Shot Domain Adaptation in Speech Recognition
• Authors: Yuang Li, Yu Wu, Jinyu Li, Shujie Liu
• Abstract summary: With only a domain-specific text prompt, we propose two zero-shot ASR domain adaptation methods using LLaMA. Experiments show that, with only one domain prompt, both methods can effectively reduce word error rates (WER) on out-of-domain TedLium-2 and SPGI datasets.
• Score: 33.07184218085399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of Language Models (LMs) has proven to be an effective way to address domain shifts in speech recognition. However, these approaches usually require a significant amount of target domain text data for the training of LMs. Different from these methods, in this work, with only a domain-specific text prompt, we propose two zero-shot ASR domain adaptation methods using LLaMA, a 7-billion-parameter large language model (LLM). LLM is used in two ways: 1) second-pass rescoring: reranking N-best hypotheses of a given ASR system with LLaMA; 2) deep LLM-fusion: incorporating LLM into the decoder of an encoder-decoder based ASR system. Experiments show that, with only one domain prompt, both methods can effectively reduce word error rates (WER) on out-of-domain TedLium-2 and SPGISpeech datasets. Especially, the deep LLM-fusion has the advantage of better recall of entity and out-of-vocabulary words.

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