  Error correction (EC) based on large language models is an emerging
technology to enhance the performance of automatic speech recognition (ASR)
systems. Generally, training data for EC are collected by automatically pairing
a large set of ASR hypotheses (as sources) and their gold references (as
targets). However, the quality of such pairs is not guaranteed, and we observed
various types of noise which can make the EC models brittle, e.g. inducing
overcorrection in out-of-domain (OOD) settings. In this work, we propose two
fundamental criteria that EC training data should satisfy: namely, EC targets
should (1) improve linguistic acceptability over sources and (2) be inferable
from the available context (e.g. source phonemes). Through these criteria, we
identify low-quality EC pairs and train the models not to make any correction
in such cases, the process we refer to as conservative data filtering. In our
experiments, we focus on Japanese ASR using a strong Conformer-CTC as the
baseline and finetune Japanese LLMs for EC. Through our evaluation on a suite
of 21 internal benchmarks, we demonstrate that our approach can significantly
reduce overcorrection and improve both the accuracy and quality of ASR results
in the challenging OOD settings.
