Wiki-En-ASR-Adapt: Large-scale synthetic dataset for English ASR
  Customization
Alexandra Antonova
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.