In this work, we propose a lightweight online approach to automatic punctuation restoration (APR), which can be utilized in speech transcription systems for, e.g., live captioning TV or radio streams. It uses only text input without prosodic features and a fine-tuned ELECTRA-Small model with a two-layer classification head. It allows for restoring question marks, commas, and periods with a very short inference time and a low latency of just three words. Our APR scheme is first tuned and compared to other architectures on a set of manual TV news transcripts. The resulting system is then compared to another real-time APR module utilizing a recurrent network and a combination of text and acoustic features. The test data we use contains automatic transcripts of radio talks and TV debates; we are also publishing this data. The results show that our APR module performs better than the above-mentioned system and yields on the two test sets an average F1 of 71.2% and 69.4%, respectively.
Cite as: Poláček, M., Červa, P., Žďánský, J., Weingartová, L. (2023) Online Punctuation Restoration using ELECTRA Model for streaming ASR Systems. Proc. INTERSPEECH 2023, 446-450, doi: 10.21437/Interspeech.2023-664
@inproceedings{polacek23_interspeech,
author={Martin Poláček and Petr Červa and Jindřich Žďánský and Lenka Weingartová},
title={{Online Punctuation Restoration using ELECTRA Model for streaming ASR Systems}},
year=2023,
booktitle={Proc. INTERSPEECH 2023},
pages={446--450},
doi={10.21437/Interspeech.2023-664}
}