  Recent advances in large language models (LLMs) have promoted generative
error correction (GER) for automatic speech recognition (ASR), which aims to
predict the ground-truth transcription from the decoded N-best hypotheses.
Thanks to the strong language generation ability of LLMs and rich information
in the N-best list, GER shows great effectiveness in enhancing ASR results.
However, it still suffers from two limitations: 1) LLMs are unaware of the
source speech during GER, which may lead to results that are grammatically
correct but violate the source speech content, 2) N-best hypotheses usually
only vary in a few tokens, making it redundant to send all of them for GER,
which could confuse LLM about which tokens to focus on and thus lead to
increased miscorrection. In this paper, we propose ClozeGER, a new paradigm for
ASR generative error correction. First, we introduce a multimodal LLM (i.e.,
SpeechGPT) to receive source speech as extra input to improve the fidelity of
correction output. Then, we reformat GER as a cloze test with logits
calibration to remove the input information redundancy and simplify GER with
clear instructions. Experiments show that ClozeGER achieves a new breakthrough
over vanilla GER on 9 popular ASR datasets.
