Optimized Tokenization for Transcribed Error Correction
Tomer Wullach Shlomo E. Chazan
The challenges facing speech recognition systems, such as variations in
pronunciations, adverse audio conditions, and the scarcity of labeled data,
emphasize the necessity for a post-processing step that corrects recurring
errors. Previous research has shown the advantages of employing dedicated error
correction models, yet training such models requires large amounts of labeled
data which is not easily obtained. To overcome this limitation, synthetic
transcribed-like data is often utilized, however, bridging the distribution gap
between transcribed errors and synthetic noise is not trivial. In this paper,
we demonstrate that the performance of correction models can be significantly
increased by training solely using synthetic data. Specifically, we empirically
show that: (1) synthetic data generated using the error distribution derived
from a set of transcribed data outperforms the common approach of applying
random perturbations; (2) applying language-specific adjustments to the
vocabulary of a BPE tokenizer strike a balance between adapting to unseen
distributions and retaining knowledge of transcribed errors. We showcase the
benefits of these key observations, and evaluate our approach using multiple
languages, speech recognition systems and prominent speech recognition
datasets.