We present a first large-scale public synthetic dataset for contextual spellchecking customization of automatic speech recognition (ASR) with focus on diverse rare and out-of-vocabulary (OOV) phrases, such as proper names or terms. The proposed approach allows creating millions of realistic examples of corrupted ASR hypotheses and simulate non-trivial biasing lists for the customization task. Furthermore, we propose injecting two types of ``hard negatives" to the simulated biasing lists in training examples and describe our procedures to automatically mine them. We report experiments with training an open-source customization model on the proposed dataset and show that the injection of hard negative biasing phrases decreases WER and the number of false alarms.
@article{arxiv.2309.17267,
title = {Wiki-En-ASR-Adapt: Large-scale synthetic dataset for English ASR Customization},
author = {Alexandra Antonova},
journal= {arXiv preprint arXiv:2309.17267},
year = {2023}
}