  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.
