  With the rapid development of speech assistants, adapting server-intended
automatic speech recognition (ASR) solutions to a direct device has become
crucial. Researchers and industry prefer to use end-to-end ASR systems for
on-device speech recognition tasks. This is because end-to-end systems can be
made resource-efficient while maintaining a higher quality compared to hybrid
systems. However, building end-to-end models requires a significant amount of
speech data. Another challenging task associated with speech assistants is
personalization, which mainly lies in handling out-of-vocabulary (OOV) words.
In this work, we consider building an effective end-to-end ASR system in
low-resource setups with a high OOV rate, embodied in Babel Turkish and Babel
Georgian tasks. To address the aforementioned problems, we propose a method of
dynamic acoustic unit augmentation based on the BPE-dropout technique. It
non-deterministically tokenizes utterances to extend the token's contexts and
to regularize their distribution for the model's recognition of unseen words.
It also reduces the need for optimal subword vocabulary size search. The
technique provides a steady improvement in regular and personalized
(OOV-oriented) speech recognition tasks (at least 6% relative WER and 25%
relative F-score) at no additional computational cost. Owing to the use of
BPE-dropout, our monolingual Turkish Conformer established a competitive result
with 22.2% character error rate (CER) and 38.9% word error rate (WER), which is
close to the best published multilingual system.
