ASR remains unsatisfactory in scenarios where the speaking style diverges
from that used to train ASR systems, resulting in erroneous transcripts. To
address this, ASR Error Correction (AEC), a post-ASR processing approach, is
required. In this work, we tackle an understudied issue: the Low-Resource
Out-of-Domain (LROOD) problem, by investigating crossmodal AEC on very limited
downstream data with 1-best hypothesis transcription. We explore pre-training
and fine-tuning strategies and uncover an ASR domain discrepancy phenomenon,
shedding light on appropriate training schemes for LROOD data. Moreover, we
propose the incorporation of discrete speech units to align with and enhance
the word embeddings for improving AEC quality. Results from multiple corpora
and several evaluation metrics demonstrate the feasibility and efficacy of our
proposed AEC approach on LROOD data, as well as its generalizability and
superiority on large-scale data. Finally, a study on speech emotion recognition
confirms that our model produces ASR error-robust transcripts suitable for
downstream applications.

ASR Error Correction (AEC) is investigated to tackle the Low-Resource Out-of-Domain (LROOD) problem, covering pre-training, fine-tuning, and incorporating discrete speech units to improve AEC quality. The proposed AEC approach demonstrates feasibility, efficacy, generalizability, superiority, and suitability for downstream applications.