2016
INTERSPEECH
INTERSPEECH 2016
Bidirectional Recurrent Neural Network with Attention Mechanism for Punctuation Restoration
Abstract
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.
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Conference Pioneer
— INTERSPEECH 2016
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Interdisciplinary Bridge
— Deep Learning and Speech & Audio
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Keyword Pioneer
— long context
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Hot Topic Early Bird
— attention mechanism
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
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Trend Setter
— Text Processing