  ASR error correction is an interesting option for post processing speech
recognition system outputs. These error correction models are usually trained
in a supervised fashion using the decoding results of a target ASR system. This
approach can be computationally intensive and the model is tuned to a specific
ASR system. Recently generative large language models (LLMs) have been applied
to a wide range of natural language processing tasks, as they can operate in a
zero-shot or few shot fashion. In this paper we investigate using ChatGPT, a
generative LLM, for ASR error correction. Based on the ASR N-best output, we
propose both unconstrained and constrained, where a member of the N-best list
is selected, approaches. Additionally, zero and 1-shot settings are evaluated.
Experiments show that this generative LLM approach can yield performance gains
for two different state-of-the-art ASR architectures, transducer and
attention-encoder-decoder based, and multiple test sets.
