Bidirectional Recurrent Neural Network with Attention Mechanism for Punctuation Restoration

Ottokar Tilk, Tanel Alumäe


Automatic speech recognition systems generally produce unpunctuated text which is difficult to read for humans and degrades the performance of many downstream machine processing tasks. This paper introduces a bidirectional recurrent neural network model with attention mechanism for punctuation restoration in unsegmented text. The model can utilize long contexts in both directions and direct attention where necessary enabling it to outperform previous state-of-the-art on English (IWSLT2011) and Estonian datasets by a large margin.


DOI: 10.21437/Interspeech.2016-1517

Cite as

Tilk, O., Alumäe, T. (2016) Bidirectional Recurrent Neural Network with Attention Mechanism for Punctuation Restoration. Proc. Interspeech 2016, 3047-3051.

Bibtex
@inproceedings{Tilk+2016,
author={Ottokar Tilk and Tanel Alumäe},
title={Bidirectional Recurrent Neural Network with Attention Mechanism for Punctuation Restoration},
year=2016,
booktitle={Interspeech 2016},
doi={10.21437/Interspeech.2016-1517},
url={http://dx.doi.org/10.21437/Interspeech.2016-1517},
pages={3047--3051}
}