  Disfluency detection models now approach high accuracy on English text.
However, little exploration has been done in improving the size and inference
time of the model. At the same time, automatic speech recognition (ASR) models
are moving from server-side inference to local, on-device inference. Supporting
models in the transcription pipeline (like disfluency detection) must follow
suit. In this work we concentrate on the disfluency detection task, focusing on
small, fast, on-device models based on the BERT architecture. We demonstrate it
is possible to train disfluency detection models as small as 1.3 MiB, while
retaining high performance. We build on previous work that showed the benefit
of data augmentation approaches such as self-training. Then, we evaluate the
effect of domain mismatch between conversational and written text on model
performance. We find that domain adaptation and data augmentation strategies
have a more pronounced effect on these smaller models, as compared to
conventional BERT models.
